Output:
Exit code: 0
Wall time: 0.8 seconds
Output:
D:\qqq\zhishiku\llmwiki\.github\workflows\test.yml
D:\qqq\zhishiku\llmwiki\.runlogs\api.err.log
D:\qqq\zhishiku\llmwiki\.runlogs\api.out.log
D:\qqq\zhishiku\llmwiki\.runlogs\mcp.err.log
D:\qqq\zhishiku\llmwiki\.runlogs\mcp.out.log
D:\qqq\zhishiku\llmwiki\.runlogs\tunnel.err.log
D:\qqq\zhishiku\llmwiki\.runlogs\tunnel.out.log
D:\qqq\zhishiku\llmwiki\.runlogs\web.err.log
D:\qqq\zhishiku\llmwiki\.runlogs\web.out.log
D:\qqq\zhishiku\llmwiki\.tools\cloudflared.exe
D:\qqq\zhishiku\llmwiki\.tools\codex.exe
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\db_print.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\db_table_names.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\xls_read.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\examples\xls_write.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\adodbapitest.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\adodbapitestconfig.py
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D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\is64bit.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\setuptestframework.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\test_adodbapi_dbapi20.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\test\tryconnection.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\adodbapi.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\ado_consts.py
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D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\is64bit.py
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D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\adodbapi\schema_table.py
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D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioboto3\resources\factory.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioboto3\resources\response.py
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D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioboto3\s3\cse.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioboto3\s3\inject.py
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D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiobotocore\eventstream.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiobotocore\handlers.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiobotocore\hooks.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiobotocore\httpchecksum.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiobotocore\httpsession.py
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D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioitertools-0.13.0.dist-info\licenses\LICENSE
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioitertools-0.13.0.dist-info\INSTALLER
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioitertools-0.13.0.dist-info\METADATA
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioitertools-0.13.0.dist-info\RECORD
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aioitertools-0.13.0.dist-info\WHEEL
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiosignal\py.typed
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiosignal\__init__.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiosignal-1.4.0.dist-info\licenses\LICENSE
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiosignal-1.4.0.dist-info\INSTALLER
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiosignal-1.4.0.dist-info\METADATA
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiosignal-1.4.0.dist-info\RECORD
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiosignal-1.4.0.dist-info\top_level.txt
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\aiosignal-1.4.0.dist-info\WHEEL
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\annotated_types\py.typed
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\annotated_types\test_cases.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\annotated_types\__init__.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\annotated_types-0.7.0.dist-info\licenses\LICENSE
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\annotated_types-0.7.0.dist-info\INSTALLER
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\annotated_types-0.7.0.dist-info\METADATA
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\annotated_types-0.7.0.dist-info\RECORD
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\annotated_types-0.7.0.dist-info\WHEEL
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\abc\_eventloop.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\abc\_resources.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\abc\_sockets.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\abc\_streams.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\abc\_subprocesses.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\abc\_tasks.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\abc\_testing.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\abc\__init__.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\streams\buffered.py
D:\qqq\zhishiku\llmwiki\api\.venv\Lib\site-packages\anyio\streams\file.py


排除表达式在 PowerShell 里没有按预期过滤 `.venv`，所以我不再做全仓递归了，改为只读取几个一方代码目录的浅层结构：`api`、`mcp`、`web`、`converter`、`supabase`、`tests`。


Get-ChildItem api -Directory | Select-Object -ExpandProperty Name

需要读取 api 顶层目录以汇总可用内容，是否允许？


Get-ChildItem api\routes,api\services,mcp,converter,supabase,tests -File -Recurse | Where-Object { $_.FullName -notmatch '([\\/])\.venv([\\/])|([\\/])__pycache__([\\/])|node_modules|([\\/])\.next([\\/])' } | Select-Object -ExpandProperty FullName

需要读取主要源码目录以汇总可用内容，是否允许？


Get-ChildItem web -File; Get-ChildItem web\app,web\components,web\lib -File -Recurse | Select-Object -First 120 -ExpandProperty FullName

需要读取 web 主要源码目录以汇总可用内容，是否允许？


Get-ChildItem api -Directory | Select-Object -ExpandProperty Name

.venv
html_parser
infra
routes
services
__pycache__


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
.venv
html_parser
infra
routes
services
__pycache__


Get-ChildItem api\routes,api\services,mcp,converter,supabase,tests -File -Recurse | Where-Object { $_.FullName -notmatch '([\\/])\.venv([\\/])|([\\/])__pycache__([\\/])|node_modules|([\\/])\.next([\\/])' } | Select-Object -ExpandProperty FullName


Output:
Exit code: 0
Wall time: 1.4 seconds
Output:
D:\qqq\zhishiku\llmwiki\api\routes\admin.py
D:\qqq\zhishiku\llmwiki\api\routes\api_keys.py
D:\qqq\zhishiku\llmwiki\api\routes\documents.py
D:\qqq\zhishiku\llmwiki\api\routes\health.py
D:\qqq\zhishiku\llmwiki\api\routes\knowledge_bases.py
D:\qqq\zhishiku\llmwiki\api\routes\me.py
D:\qqq\zhishiku\llmwiki\api\routes\usage.py
D:\qqq\zhishiku\llmwiki\api\routes\__init__.py
D:\qqq\zhishiku\llmwiki\api\services\chunker.py
D:\qqq\zhishiku\llmwiki\api\services\ocr.py
D:\qqq\zhishiku\llmwiki\api\services\s3.py
D:\qqq\zhishiku\llmwiki\mcp\auth.py
D:\qqq\zhishiku\llmwiki\mcp\config.py
D:\qqq\zhishiku\llmwiki\mcp\db.py
D:\qqq\zhishiku\llmwiki\mcp\Dockerfile
D:\qqq\zhishiku\llmwiki\mcp\railway.toml
D:\qqq\zhishiku\llmwiki\mcp\requirements.txt
D:\qqq\zhishiku\llmwiki\mcp\server.py
D:\qqq\zhishiku\llmwiki\mcp\__init__.py
D:\qqq\zhishiku\llmwiki\mcp\tools\delete.py
D:\qqq\zhishiku\llmwiki\mcp\tools\guide.py
D:\qqq\zhishiku\llmwiki\mcp\tools\helpers.py
D:\qqq\zhishiku\llmwiki\mcp\tools\read.py
D:\qqq\zhishiku\llmwiki\mcp\tools\search.py
D:\qqq\zhishiku\llmwiki\mcp\tools\write.py
D:\qqq\zhishiku\llmwiki\mcp\tools\__init__.py
D:\qqq\zhishiku\llmwiki\converter\Dockerfile
D:\qqq\zhishiku\llmwiki\converter\main.py
D:\qqq\zhishiku\llmwiki\converter\railway.toml
D:\qqq\zhishiku\llmwiki\converter\requirements.txt
D:\qqq\zhishiku\llmwiki\supabase\.gitignore
D:\qqq\zhishiku\llmwiki\supabase\config.toml
D:\qqq\zhishiku\llmwiki\supabase\.branches\_current_branch
D:\qqq\zhishiku\llmwiki\supabase\.temp\cli-latest
D:\qqq\zhishiku\llmwiki\supabase\migrations\001_initial.sql
D:\qqq\zhishiku\llmwiki\tests\conftest.py
D:\qqq\zhishiku\llmwiki\tests\requirements.txt
D:\qqq\zhishiku\llmwiki\tests\__init__.py
D:\qqq\zhishiku\llmwiki\tests\helpers\jwt.py
D:\qqq\zhishiku\llmwiki\tests\helpers\schema.sql
D:\qqq\zhishiku\llmwiki\tests\helpers\__init__.py
D:\qqq\zhishiku\llmwiki\tests\integration\conftest.py
D:\qqq\zhishiku\llmwiki\tests\integration\__init__.py
D:\qqq\zhishiku\llmwiki\tests\integration\isolation\conftest.py
D:\qqq\zhishiku\llmwiki\tests\integration\isolation\test_api_isolation.py
D:\qqq\zhishiku\llmwiki\tests\integration\isolation\__init__.py
D:\qqq\zhishiku\llmwiki\tests\unit\test_chunker.py
D:\qqq\zhishiku\llmwiki\tests\unit\test_helpers.py
D:\qqq\zhishiku\llmwiki\tests\unit\__init__.py


Get-ChildItem web -File; Get-ChildItem web\app,web\components,web\lib -File -Recurse | Select-Object -First 120 -ExpandProperty FullName


Output:
Exit code: 124
Wall time: 10.1 seconds
Output:
command timed out after 10066 milliseconds

    目录: D:\qqq\zhishiku\llmwiki\web

Mode                 LastWriteTime         Length Name
----                 -------------         ------ ----
-a----          2026/4/9     11:57            319 .env.local
-a----          2026/4/9     11:22            452 components.json
-a----          2026/4/9     11:22           2552 deno.lock
-a----          2026/4/9     11:22            333 Dockerfile
-a----          2026/4/9     11:22            413 instrumentation.ts
-a----         2026/4/10     10:14            257 next-env.d.ts
-a----          2026/4/9     11:22            327 next.config.ts
-a----          2026/4/9     11:40         416601 package-lock.json
-a----          2026/4/9     11:22           2037 package.json
-a----          2026/4/9     11:22             99 postcss.config.mjs
-a----          2026/4/9     11:22            101 railway.toml
-a----          2026/4/9     11:22            273 sentry.client.config.ts
-a----          2026/4/9     11:22            160 sentry.edge.config.ts
-a----          2026/4/9     11:22            160 sentry.server.config.ts
-a----          2026/4/9     11:22            743 tsconfig.json
-a----          2026/4/9     11:22         454970 tsconfig.tsbuildinfo


主要结构已经清楚了，而且仓库里存在 `mcp/tools/guide.py`。我读取它的实现，看看实际 `guide` 会返回哪些内容，而不是只根据 README 猜。


Get-Content mcp\tools\guide.py -TotalCount 260

需要读取 guide 工具实现来准确说明当前知识库可用内容，是否允许？


Get-Content mcp\tools\guide.py -TotalCount 260

from mcp.server.fastmcp import FastMCP, Context

from config import settings
from db import scoped_query
from .helpers import get_user_id

GUIDE_TEXT = """# LLM Wiki 鈥?How It Works

榛樿杈撳嚭璇█锛氫腑鏂囷紙绠€浣擄級銆傞櫎闈炵敤鎴锋槑纭姹傚叾浠栬瑷€锛屽惁鍒欒浣跨敤涓枃瀹屾垚闂瓟涓?Wiki 椤甸潰鎾板啓銆?
You are connected to an **LLM Wiki** 鈥?a personal knowledge workspace where you compile and maintain a structured wiki from raw source documents.

## Architecture

1. **Raw Sources** (path: `/`) 鈥?uploaded documents (PDFs, notes, images, spreadsheets). Source of truth. Read-only.
2. **Compiled Wiki** (path: `/wiki/`) 鈥?markdown pages YOU create and maintain. You own this layer.
3. **Tools** 鈥?`search`, `read`, `write`, `delete` 鈥?your interface to both layers.

## Wiki Structure

Every wiki follows this structure. These categories are not suggestions 鈥?they are the backbone of the wiki.

### Overview (`/wiki/overview.md`) 鈥?THE HUB PAGE
Always exists. This is the front page of the wiki. It must contain:
- A summary of what this wiki covers and its scope
- **Source count** and page count (update on every ingest)
- **Key Findings** 鈥?the most important insights across all sources
- **Recent Updates** 鈥?last 5-10 actions (ingests, new pages, revisions)

Update the Overview after EVERY ingest or major edit. If you only update one page, it should be this one.

### Concepts (`/wiki/concepts/`) 鈥?ABSTRACT IDEAS
Pages for theoretical frameworks, methodologies, principles, themes 鈥?anything conceptual.
- `/wiki/concepts/scaling-laws.md`
- `/wiki/concepts/attention-mechanisms.md`
- `/wiki/concepts/self-supervised-learning.md`

Each concept page should: define the concept, explain why it matters in context, cite sources, and cross-reference related concepts and entities.

### Entities (`/wiki/entities/`) 鈥?CONCRETE THINGS
Pages for people, organizations, products, technologies, papers, datasets 鈥?anything you can point to.
- `/wiki/entities/transformer.md`
- `/wiki/entities/openai.md`
- `/wiki/entities/attention-is-all-you-need.md`

Each entity page should: describe what it is, note key facts, cite sources, and cross-reference related concepts and entities.

### Log (`/wiki/log.md`) 鈥?CHRONOLOGICAL RECORD
Always exists. Append-only. Records every ingest, major edit, and lint pass. Never delete entries.

Format 鈥?each entry starts with a parseable header:
```
## [YYYY-MM-DD] ingest | Source Title
- Created concept page: [Page Title](concepts/page.md)
- Updated entity page: [Page Title](entities/page.md)
- Updated overview with new findings
- Key takeaway: one sentence summary

## [YYYY-MM-DD] query | Question Asked
- Created new page: [Page Title](concepts/page.md)
- Finding: one sentence answer

## [YYYY-MM-DD] lint | Health Check
- Fixed contradiction between X and Y
- Added missing cross-reference in Z
```

### Additional Pages
You can create pages outside of concepts/ and entities/ when needed:
- `/wiki/comparisons/x-vs-y.md` 鈥?for deep comparisons
- `/wiki/timeline.md` 鈥?for chronological narratives

But concepts/ and entities/ are the primary categories. When in doubt, file there.

## Page Hierarchy

Wiki pages use a parent/child hierarchy via paths:
- `/wiki/concepts.md` 鈥?parent page (optional; summarizes all concepts)
- `/wiki/concepts/attention.md` 鈥?child page

Parent pages summarize; child pages go deep. The UI renders this as an expandable tree.

## Writing Standards

**Wiki pages must be substantially richer than a chat response.** They are persistent, curated artifacts.

### Structure
- Start with a summary paragraph (no H1 鈥?the title is rendered by the UI)
- Use `##` for major sections, `###` for subsections
- One idea per section. Bullet points for facts, prose for synthesis.

### Visual Elements 鈥?MANDATORY

**Every wiki page MUST include at least one visual element.** A page with only prose is incomplete.

**Mermaid diagrams** 鈥?use for ANY structured relationship:
- Flowcharts for processes, pipelines, decision trees
- Sequence diagrams for interactions, timelines
- Quadrant charts for comparisons, trade-off analyses
- Entity relationship diagrams for people, companies, concepts

````
```mermaid
graph LR
    A[Input] --> B[Process] --> C[Output]
```
````

**Tables** 鈥?use for ANY structured comparison:
- Feature matrices, pros/cons, timelines, metrics
- If you're listing 3+ items with attributes, it should be a table

**SVG assets** 鈥?for custom visuals Mermaid can't express:
- Create: `write(command="create", path="/wiki/", title="diagram.svg", content="<svg>...</svg>", tags=["diagram"])`
- Embed in wiki pages: `![Description](diagram.svg)`

### Citations 鈥?REQUIRED

Every factual claim MUST cite its source via markdown footnotes:
```
Transformers use self-attention[^1] that scales quadratically[^2].

[^1]: attention-paper.pdf, p.3
[^2]: scaling-laws.pdf, p.12-14
```

Rules:
- Use the FULL source filename 鈥?never truncate
- Add page numbers for PDFs: `paper.pdf, p.3`
- One citation per claim 鈥?don't batch unrelated claims
- Citations render as hoverable popover badges in the UI

### Cross-References
Link between wiki pages using standard markdown links to other wiki paths.

## Core Workflows

### Ingest a New Source
1. Read it: `read(path="source.pdf", pages="1-10")`
2. Discuss key takeaways with the user
3. Create or update **concept** pages under `/wiki/concepts/`
4. Create or update **entity** pages under `/wiki/entities/`
5. Update `/wiki/overview.md` 鈥?source count, key findings, recent updates
6. Append an entry to `/wiki/log.md`
7. A single source typically touches 5-15 wiki pages 鈥?that's expected

### Answer a Question
1. `search(mode="search", query="term")` to find relevant content
2. Read relevant wiki pages and sources
3. Synthesize with citations
4. If the answer is valuable, file it as a new wiki page 鈥?explorations should compound
5. Append a query entry to `/wiki/log.md`

### Maintain the Wiki (Lint)
Check for: contradictions, orphan pages, missing cross-references, stale claims, concepts mentioned but lacking their own page. Append a lint entry to `/wiki/log.md`.

## Available Knowledge Bases

"""

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="guide",
        description="Get started with LLM Wiki. Call this to understand how the knowledge vault works and see your available knowledge bases.",
    )
    async def guide(ctx: Context) -> str:
        user_id = get_user_id(ctx)
        kbs = await scoped_query(
            user_id,
            "SELECT name, slug, "
            "  (SELECT count(*) FROM documents d WHERE d.knowledge_base_id = kb.id AND d.path NOT LIKE '/wiki/%%' AND NOT d.archived) as source_count, "
            "  (SELECT count(*) FROM documents d WHERE d.knowledge_base_id = kb.id AND d.path LIKE '/wiki/%%' AND NOT d.archived) as wiki_count "
            "FROM knowledge_bases kb ORDER BY created_at DESC",
        )
        if not kbs:
            return GUIDE_TEXT + "No knowledge bases yet. Create one at " + settings.APP_URL + "/wikis"

        lines = []
        for kb in kbs:
            lines.append(f"- **{kb['name']}** (`{kb['slug']}`) 鈥?{kb['source_count']} sources, {kb['wiki_count']} wiki pages")
        return GUIDE_TEXT + "\n".join(lines)


Output:
Exit code: 0
Wall time: 0.6 seconds
Output:
from mcp.server.fastmcp import FastMCP, Context

from config import settings
from db import scoped_query
from .helpers import get_user_id

GUIDE_TEXT = """# LLM Wiki 鈥?How It Works

榛樿杈撳嚭璇█锛氫腑鏂囷紙绠€浣擄級銆傞櫎闈炵敤鎴锋槑纭姹傚叾浠栬瑷€锛屽惁鍒欒浣跨敤涓枃瀹屾垚闂瓟涓?Wiki 椤甸潰鎾板啓銆?
You are connected to an **LLM Wiki** 鈥?a personal knowledge workspace where you compile and maintain a structured wiki from raw source documents.

## Architecture

1. **Raw Sources** (path: `/`) 鈥?uploaded documents (PDFs, notes, images, spreadsheets). Source of truth. Read-only.
2. **Compiled Wiki** (path: `/wiki/`) 鈥?markdown pages YOU create and maintain. You own this layer.
3. **Tools** 鈥?`search`, `read`, `write`, `delete` 鈥?your interface to both layers.

## Wiki Structure

Every wiki follows this structure. These categories are not suggestions 鈥?they are the backbone of the wiki.

### Overview (`/wiki/overview.md`) 鈥?THE HUB PAGE
Always exists. This is the front page of the wiki. It must contain:
- A summary of what this wiki covers and its scope
- **Source count** and page count (update on every ingest)
- **Key Findings** 鈥?the most important insights across all sources
- **Recent Updates** 鈥?last 5-10 actions (ingests, new pages, revisions)

Update the Overview after EVERY ingest or major edit. If you only update one page, it should be this one.

### Concepts (`/wiki/concepts/`) 鈥?ABSTRACT IDEAS
Pages for theoretical frameworks, methodologies, principles, themes 鈥?anything conceptual.
- `/wiki/concepts/scaling-laws.md`
- `/wiki/concepts/attention-mechanisms.md`
- `/wiki/concepts/self-supervised-learning.md`

Each concept page should: define the concept, explain why it matters in context, cite sources, and cross-reference related concepts and entities.

### Entities (`/wiki/entities/`) 鈥?CONCRETE THINGS
Pages for people, organizations, products, technologies, papers, datasets 鈥?anything you can point to.
- `/wiki/entities/transformer.md`
- `/wiki/entities/openai.md`
- `/wiki/entities/attention-is-all-you-need.md`

Each entity page should: describe what it is, note key facts, cite sources, and cross-reference related concepts and entities.

### Log (`/wiki/log.md`) 鈥?CHRONOLOGICAL RECORD
Always exists. Append-only. Records every ingest, major edit, and lint pass. Never delete entries.

Format 鈥?each entry starts with a parseable header:
```
## [YYYY-MM-DD] ingest | Source Title
- Created concept page: [Page Title](concepts/page.md)
- Updated entity page: [Page Title](entities/page.md)
- Updated overview with new findings
- Key takeaway: one sentence summary

## [YYYY-MM-DD] query | Question Asked
- Created new page: [Page Title](concepts/page.md)
- Finding: one sentence answer

## [YYYY-MM-DD] lint | Health Check
- Fixed contradiction between X and Y
- Added missing cross-reference in Z
```

### Additional Pages
You can create pages outside of concepts/ and entities/ when needed:
- `/wiki/comparisons/x-vs-y.md` 鈥?for deep comparisons
- `/wiki/timeline.md` 鈥?for chronological narratives

But concepts/ and entities/ are the primary categories. When in doubt, file there.

## Page Hierarchy

Wiki pages use a parent/child hierarchy via paths:
- `/wiki/concepts.md` 鈥?parent page (optional; summarizes all concepts)
- `/wiki/concepts/attention.md` 鈥?child page

Parent pages summarize; child pages go deep. The UI renders this as an expandable tree.

## Writing Standards

**Wiki pages must be substantially richer than a chat response.** They are persistent, curated artifacts.

### Structure
- Start with a summary paragraph (no H1 鈥?the title is rendered by the UI)
- Use `##` for major sections, `###` for subsections
- One idea per section. Bullet points for facts, prose for synthesis.

### Visual Elements 鈥?MANDATORY

**Every wiki page MUST include at least one visual element.** A page with only prose is incomplete.

**Mermaid diagrams** 鈥?use for ANY structured relationship:
- Flowcharts for processes, pipelines, decision trees
- Sequence diagrams for interactions, timelines
- Quadrant charts for comparisons, trade-off analyses
- Entity relationship diagrams for people, companies, concepts

````
```mermaid
graph LR
    A[Input] --> B[Process] --> C[Output]
```
````

**Tables** 鈥?use for ANY structured comparison:
- Feature matrices, pros/cons, timelines, metrics
- If you're listing 3+ items with attributes, it should be a table

**SVG assets** 鈥?for custom visuals Mermaid can't express:
- Create: `write(command="create", path="/wiki/", title="diagram.svg", content="<svg>...</svg>", tags=["diagram"])`
- Embed in wiki pages: `![Description](diagram.svg)`

### Citations 鈥?REQUIRED

Every factual claim MUST cite its source via markdown footnotes:
```
Transformers use self-attention[^1] that scales quadratically[^2].

[^1]: attention-paper.pdf, p.3
[^2]: scaling-laws.pdf, p.12-14
```

Rules:
- Use the FULL source filename 鈥?never truncate
- Add page numbers for PDFs: `paper.pdf, p.3`
- One citation per claim 鈥?don't batch unrelated claims
- Citations render as hoverable popover badges in the UI

### Cross-References
Link between wiki pages using standard markdown links to other wiki paths.

## Core Workflows

### Ingest a New Source
1. Read it: `read(path="source.pdf", pages="1-10")`
2. Discuss key takeaways with the user
3. Create or update **concept** pages under `/wiki/concepts/`
4. Create or update **entity** pages under `/wiki/entities/`
5. Update `/wiki/overview.md` 鈥?source count, key findings, recent updates
6. Append an entry to `/wiki/log.md`
7. A single source typically touches 5-15 wiki pages 鈥?that's expected

### Answer a Question
1. `search(mode="search", query="term")` to find relevant content
2. Read relevant wiki pages and sources
3. Synthesize with citations
4. If the answer is valuable, file it as a new wiki page 鈥?explorations should compound
5. Append a query entry to `/wiki/log.md`

### Maintain the Wiki (Lint)
Check for: contradictions, orphan pages, missing cross-references, stale claims, concepts mentioned but lacking their own page. Append a lint entry to `/wiki/log.md`.

## Available Knowledge Bases

"""

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="guide",
        description="Get started with LLM Wiki. Call this to understand how the knowledge vault works and see your available knowledge bases.",
    )
    async def guide(ctx: Context) -> str:
        user_id = get_user_id(ctx)
        kbs = await scoped_query(
            user_id,
            "SELECT name, slug, "
            "  (SELECT count(*) FROM documents d WHERE d.knowledge_base_id = kb.id AND d.path NOT LIKE '/wiki/%%' AND NOT d.archived) as source_count, "
            "  (SELECT count(*) FROM documents d WHERE d.knowledge_base_id = kb.id AND d.path LIKE '/wiki/%%' AND NOT d.archived) as wiki_count "
            "FROM knowledge_bases kb ORDER BY created_at DESC",
        )
        if not kbs:
            return GUIDE_TEXT + "No knowledge bases yet. Create one at " + settings.APP_URL + "/wikis"

        lines = []
        for kb in kbs:
            lines.append(f"- **{kb['name']}** (`{kb['slug']}`) 鈥?{kb['source_count']} sources, {kb['wiki_count']} wiki pages")
        return GUIDE_TEXT + "\n".join(lines)


我这边当前没有可直接调用的 `guide` 工具，MCP 资源列表也是空的；所以我不能真正执行 LLM Wiki 的 `guide` MCP 调用。仓库里确实实现了这个工具：[mcp/tools/guide.py](D:/qqq/zhishiku/llmwiki/mcp/tools/guide.py)，它会查询数据库里的 `knowledge_bases`，返回每个知识库的名称、slug、source 数量和 wiki 页面数量。

当前我能看到的本地内容是这个 LLM Wiki 项目本身：

- `web/`：Next.js 前端，知识库 UI、文档/页面展示等。
- `api/`：FastAPI 后端，包含 `documents`、`knowledge_bases`、`api_keys`、`usage` 等路由。
- `mcp/`：MCP 服务端，包含 `guide`、`search`、`read`、`write`、`delete` 工具实现。
- `converter/`：Office 转 PDF 的独立转换服务。
- `supabase/`：数据库配置和初始迁移。
- `tests/`：单元测试与隔离集成测试。
- 文档/脚本：`README.md`、`OPENAI_GPT54_使用说明.md`、`start-local.ps1`、`stop-local.ps1`、`run-openai-gpt54-mcp.ps1` 等。

下一步建议：先启动本地服务并把 MCP connector 接上，这样才能真正调用 `guide` 并列出数据库里的实际知识库。最直接的下一步是检查 `.env` 配置后运行 `.\start-local.ps1`，然后连接 `http://localhost:8080/mcp`。


# Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

In Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions. If you absolutely must ask a question because the answer cannot be discovered from local context and a reasonable assumption would be risky, ask the user directly with a concise plain-text question. Never write a multiple choice question as a textual assistant message.


你现在是我的知识库编译助手。请按顺序执行：
1) 调用 guide 了解可用知识库。
2) 列出资料区所有 ready 文件。
3) 读取我最新上传的两份 PDF。
4) 输出中文总结：核心观点、可落地建议、风险与争议。
5) 将总结写入 wiki 的 overview.md（若已存在则更新）。
完成后告诉我你更新了哪些页面。


我会按这个流程尝试执行，但先说明边界：当前会话没有直接暴露 LLM Wiki 的 MCP `guide/search/read/write` 工具，所以我需要通过仓库里的 MCP/API 实现或数据库连接来完成同等操作。我先读取 MCP 工具定义，确认如何安全地调用知识库数据。


需要读取 guide 工具实现以按用户要求了解可用知识库，是否允许？


Get-Content mcp\tools\search.py -TotalCount 320

需要读取 search 工具实现以列出 ready 文件，是否允许？


Get-Content mcp\tools\read.py -TotalCount 360

需要读取 read 工具实现以读取 PDF，是否允许？


Get-Content mcp\tools\write.py -TotalCount 360

需要读取 write 工具实现以更新 overview.md，是否允许？


Get-Content mcp\tools\search.py -TotalCount 320

import logging
from typing import Literal

from mcp.server.fastmcp import FastMCP, Context

from db import scoped_query, scoped_queryrow
from .helpers import get_user_id, resolve_kb, deep_link, glob_match, MAX_LIST, MAX_SEARCH

logger = logging.getLogger(__name__)

CONTEXT_CHARS = 120

def _extract_snippet(content: str, query: str) -> str:
    if not content:
        return "(empty)"
    idx = content.lower().find(query.lower())
    if idx < 0:
        return content[:CONTEXT_CHARS * 2].strip()
    start = max(0, idx - CONTEXT_CHARS)
    end = min(len(content), idx + len(query) + CONTEXT_CHARS)
    snippet = content[start:end].strip()
    if start > 0:
        snippet = "..." + snippet
    if end < len(content):
        snippet = snippet + "..."
    return snippet

async def _list_all_kbs(user_id: str) -> str:
    kbs = await scoped_query(
        user_id,
        "SELECT name, slug, created_at FROM knowledge_bases ORDER BY created_at DESC",
    )
    if not kbs:
        return "No knowledge bases found. Create one first."

    lines = ["**Knowledge Bases:**\n"]
    for kb in kbs:
        doc_count = await scoped_queryrow(
            user_id,
            "SELECT count(*) as cnt FROM documents WHERE knowledge_base_id = ("
            "SELECT id FROM knowledge_bases WHERE slug = $1) AND NOT archived",
            kb["slug"],
        )
        cnt = doc_count["cnt"] if doc_count else 0
        lines.append(f"  {kb['slug']}/ 鈥?{kb['name']} ({cnt} documents)")
    return "\n".join(lines)

async def _list_documents(user_id: str, kb: dict, target: str, tags: list[str] | None) -> str:
    docs = await scoped_query(
        user_id,
        "SELECT id, filename, title, path, file_type, tags, page_count, updated_at "
        "FROM documents WHERE knowledge_base_id = $1 AND NOT archived "
        "ORDER BY path, filename",
        kb["id"],
    )

    if target not in ("*", "**", "**/*"):
        glob_pat = "/" + target.lstrip("/") if not target.startswith("/") else target
        docs = [d for d in docs if glob_match(d["path"] + d["filename"], glob_pat)]

    if tags:
        tag_set = {t.lower() for t in tags}
        docs = [d for d in docs if tag_set.issubset({t.lower() for t in (d["tags"] or [])})]

    if not docs:
        return f"No matches for `{target}` in {kb['slug']}."

    sources = [d for d in docs if not d["path"].startswith("/wiki/")]
    wiki_pages = [d for d in docs if d["path"].startswith("/wiki/")]

    lines = [f"**{kb['name']}** (`{target}`):\n"]

    if sources:
        lines.append(f"**Sources ({len(sources)}):**")
        for doc in sources[:MAX_LIST]:
            tag_str = f" [{', '.join(doc['tags'])}]" if doc["tags"] else ""
            date_part = f", {doc['updated_at'].strftime('%Y-%m-%d')}" if doc["updated_at"] else ""
            pages_part = f", {doc['page_count']}p" if doc["page_count"] else ""
            lines.append(f"  {doc['path']}{doc['filename']} ({doc['file_type']}{pages_part}{date_part}){tag_str}")
        if len(sources) > MAX_LIST:
            lines.append(f"  ... {len(sources) - MAX_LIST} more")

    if wiki_pages:
        if sources:
            lines.append("")
        lines.append(f"**Wiki ({len(wiki_pages)} pages):**")
        for doc in wiki_pages[:MAX_LIST]:
            date_part = f", {doc['updated_at'].strftime('%Y-%m-%d')}" if doc["updated_at"] else ""
            lines.append(f"  {doc['path']}{doc['filename']}{date_part}")

    return "\n".join(lines)

async def _search_chunks(
    user_id: str, kb: dict, query: str, path: str,
    tags: list[str] | None, limit: int,
) -> str:
    path_filter = ""
    if path not in ("*", "**", "**/*"):
        if path.startswith("/wiki"):
            path_filter = " AND d.path LIKE '/wiki/%%'"
        elif path == "/" or path == "/*":
            path_filter = " AND d.path NOT LIKE '/wiki/%%'"

    matches = await scoped_query(
        user_id,
        f"SELECT dc.content, dc.page, dc.header_breadcrumb, dc.chunk_index, "
        f"  d.filename, d.title, d.path, d.file_type, d.tags, "
        f"  pgroonga_score(dc.tableoid, dc.ctid) AS score "
        f"FROM document_chunks dc "
        f"JOIN documents d ON dc.document_id = d.id "
        f"WHERE dc.knowledge_base_id = $1 "
        f"  AND dc.content &@~ $2 "
        f"  AND NOT d.archived"
        f"{path_filter} "
        f"ORDER BY score DESC, dc.chunk_index "
        f"LIMIT {limit}",
        kb["id"], query,
    )

    if tags:
        tag_set = {t.lower() for t in tags}
        matches = [m for m in matches if tag_set.issubset({t.lower() for t in (m.get("tags") or [])})]

    if not matches:
        return f"No matches for `{query}` in {kb['slug']}."

    lines = [f"**{len(matches)} result(s)** for `{query}`:\n"]
    for m in matches:
        filepath = f"{m['path']}{m['filename']}"
        page_str = f" (p.{m['page']})" if m['page'] else ""
        breadcrumb = f"\n  {m['header_breadcrumb']}" if m["header_breadcrumb"] else ""
        snippet = _extract_snippet(m["content"], query)
        link = deep_link(kb["slug"], m["path"], m["filename"])
        score = m.get("score", 0)
        score_str = f" [{score:.1f}]" if score else ""
        lines.append(f"**{filepath}**{page_str}{score_str} 鈥?[view]({link}){breadcrumb}")
        lines.append(f"```\n{snippet}\n```\n")

    return "\n".join(lines)

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="search",
        description=(
            "Browse or search the knowledge vault.\n\n"
            "Sources (raw documents) live at `/`. Wiki pages (LLM-compiled) live at `/wiki/`.\n\n"
            "Modes:\n"
            "- list: browse files and folders\n"
            "- search: keyword search across document content (searches chunks for precise results with page numbers)\n\n"
            "Use `path` to scope: `*` for root, `/wiki/**` for wiki only, `*.pdf` for PDFs, etc.\n"
            "Use `tags` to filter by document tags."
        ),
    )
    async def search(
        ctx: Context,
        knowledge_base: str,
        mode: Literal["list", "search"] = "list",
        query: str = "",
        path: str = "*",
        tags: list[str] | None = None,
        limit: int = 10,
    ) -> str:
        user_id = get_user_id(ctx)

        if not knowledge_base:
            return await _list_all_kbs(user_id)

        kb = await resolve_kb(user_id, knowledge_base)
        if not kb:
            return f"Knowledge base '{knowledge_base}' not found."

        if mode == "list":
            return await _list_documents(user_id, kb, path, tags)
        elif mode == "search":
            if not query:
                return "search mode requires a query."
            return await _search_chunks(user_id, kb, query, path, tags, min(limit, MAX_SEARCH))

        return f"Unknown mode: {mode}"


Get-Content mcp\tools\read.py -TotalCount 360

import base64
import json
import logging

from mcp.server.fastmcp import FastMCP, Context
from mcp.types import TextContent, ImageContent

from db import scoped_query, scoped_queryrow
from .helpers import (
    get_user_id, resolve_kb, deep_link, resolve_path,
    load_s3_bytes, parse_page_range, glob_match,
)

logger = logging.getLogger(__name__)

MAX_BATCH_CHARS = 120_000

_IMG_MIME = {
    "png": "image/png",
    "jpg": "image/jpeg",
    "jpeg": "image/jpeg",
    "webp": "image/webp",
    "gif": "image/gif",
}

def _text(s: str) -> TextContent:
    return TextContent(type="text", text=s)

def _image(data: bytes, fmt: str) -> ImageContent:
    return ImageContent(
        type="image",
        data=base64.b64encode(data).decode(),
        mimeType=_IMG_MIME.get(fmt, f"image/{fmt}"),
    )

def _extract_sections(content: str, section_names: list[str]) -> str:
    lines = content.split("\n")
    sections = []
    current_section = None
    current_lines = []

    for line in lines:
        if line.startswith("#"):
            if current_section and current_lines:
                sections.append((current_section, "\n".join(current_lines)))
            heading = line.lstrip("#").strip()
            current_section = heading
            current_lines = [line]
        elif current_section:
            current_lines.append(line)

    if current_section and current_lines:
        sections.append((current_section, "\n".join(current_lines)))

    wanted = {s.lower() for s in section_names}
    matched = [text for name, text in sections if name.lower() in wanted]

    if not matched:
        return f"No sections matching {section_names} found."
    return "\n\n".join(matched)

async def _read_pages(
    doc: dict, kb: dict, header: str, pages_str: str, include_images: bool = False,
) -> str | list:
    max_page = doc["page_count"] or 1
    page_nums = parse_page_range(pages_str, max_page)
    if not page_nums:
        return header + f"Invalid page range: {pages_str} (document has {max_page} pages)"

    user_id = str(doc["user_id"])
    doc_id = str(doc["id"])

    page_rows = await scoped_query(
        user_id,
        "SELECT page, content, elements FROM document_pages "
        "WHERE document_id = $1 AND page = ANY($2) ORDER BY page",
        doc["id"], page_nums,
    )

    if not page_rows:
        return header + f"No page data found for pages {pages_str}."

    content_blocks: list[TextContent | ImageContent] = [_text(header)]
    has_images = False

    for row in page_rows:
        content_blocks.append(_text(f"**鈥?Page {row['page']} 鈥?*\n\n{row['content']}"))

        if not include_images:
            continue

        elements = row["elements"]
        if not elements:
            continue
        if isinstance(elements, str):
            elements = json.loads(elements)

        images = elements.get("images", [])
        for img_meta in images:
            img_id = img_meta.get("id")
            if not img_id:
                continue
            s3_key = f"{user_id}/{doc_id}/images/{img_id}"
            img_bytes = await load_s3_bytes(s3_key)
            if img_bytes:
                fmt = "jpeg" if img_id.endswith((".jpg", ".jpeg")) else "png"
                content_blocks.append(_image(img_bytes, fmt))
                has_images = True

    if has_images:
        return content_blocks
    return "\n\n".join(b.text for b in content_blocks)

async def _read_spreadsheet_index(doc: dict, header: str) -> str:
    user_id = str(doc["user_id"])
    page_rows = await scoped_query(
        user_id,
        "SELECT page, content, elements FROM document_pages "
        "WHERE document_id = $1 ORDER BY page",
        doc["id"],
    )
    if not page_rows:
        return header + (doc["content"] or "(no data)")

    lines = [header, "**Sheets:**\n"]
    for row in page_rows:
        elements = row["elements"]
        if isinstance(elements, str):
            elements = json.loads(elements)
        sheet_name = (elements or {}).get("sheet_name", f"Sheet {row['page']}")
        row_count = row["content"].count("\n") if row["content"] else 0
        lines.append(f"  Page {row['page']}: **{sheet_name}** (~{row_count} rows)")
    lines.append(f"\nUse `pages=\"1\"` to read a specific sheet.")
    return "\n".join(lines)

async def _read_batch(user_id: str, kb: dict, path: str) -> str:
    docs = await scoped_query(
        user_id,
        "SELECT id, filename, title, path, content, tags, file_type, page_count "
        "FROM documents WHERE knowledge_base_id = $1 AND NOT archived "
        "ORDER BY path, filename",
        kb["id"],
    )

    glob_pat = "/" + path.lstrip("/") if not path.startswith("/") else path
    docs = [d for d in docs if glob_match(d["path"] + d["filename"], glob_pat)]

    if not docs:
        return f"No documents matching `{p…2473 chars truncated…    header += f"\n*{len(skipped_docs)} more document(s) beyond budget 鈥?read individually*"
    header += "\n\n---\n\n"

    return header + "\n\n---\n\n".join(parts)

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="read",
        description=(
            "Read document content from the knowledge vault.\n\n"
            "Accepts a single file path OR a glob pattern to batch-read multiple files:\n"
            "- `path=\"notes.md\"` 鈥?read one file\n"
            "- `path=\"*.md\"` 鈥?read all markdown files in root\n"
            "- `path=\"/wiki/**\"` 鈥?read all wiki pages\n"
            "- `path=\"**/*.md\"` 鈥?read all markdown files everywhere\n\n"
            "Batch reads are the PREFERRED way to read multiple documents at once 鈥?use them generously.\n"
            "Glob reads sample the first few pages from each document (including PDFs) up to a 120k char budget. "
            "This gives you a broad overview of an entire folder in one call. Read individual files for full content.\n\n"
            "For PDFs and office docs, use `pages` to read specific page ranges (e.g. '1-50', '3', '10-30').\n"
            "You can read up to 50+ pages in a single call 鈥?use wide ranges to avoid unnecessary round trips.\n"
            "For spreadsheets, each sheet is a page (call without pages first to see sheet names).\n"
            "Set `include_images=true` to include embedded images (e.g. figures in PDFs, standalone image files). "
            "Off by default to save context 鈥?enable when you need to see charts, diagrams, or photos.\n\n"
            "When reading sources to compile wiki pages, note the filename and page ranges for citation."
        ),
        structured_output=False,
    )
    async def read(
        ctx: Context,
        knowledge_base: str,
        path: str,
        pages: str = "",
        sections: list[str] | None = None,
        include_images: bool = False,
    ) -> str | list:
        user_id = get_user_id(ctx)

        kb = await resolve_kb(user_id, knowledge_base)
        if not kb:
            return f"Knowledge base '{knowledge_base}' not found."

        is_glob = "*" in path or "?" in path
        if is_glob:
            return await _read_batch(user_id, kb, path)

        dir_path, filename = resolve_path(path)

        doc = await scoped_queryrow(
            user_id,
            "SELECT id, user_id, filename, title, path, content, tags, version, file_type, "
            "page_count, created_at, updated_at "
            "FROM documents WHERE knowledge_base_id = $1 AND filename = $2 AND path = $3 AND NOT archived",
            kb["id"], filename, dir_path,
        )
        if not doc:
            doc = await scoped_queryrow(
                user_id,
                "SELECT id, user_id, filename, title, path, content, tags, version, file_type, "
                "page_count, created_at, updated_at "
                "FROM documents WHERE knowledge_base_id = $1 AND (filename = $2 OR title = $2) AND NOT archived",
                kb["id"], path.lstrip("/").split("/")[-1],
            )

        if not doc:
            return f"Document '{path}' not found in {knowledge_base}."

        tags_str = ", ".join(doc["tags"]) if doc["tags"] else "none"
        link = deep_link(kb["slug"], doc["path"], doc["filename"])
        file_type = doc["file_type"] or ""

        header = (
            f"**{doc['title'] or doc['filename']}**\n"
            f"Type: {file_type} | Tags: {tags_str} | Version: {doc['version']} | "
            f"Updated: {doc['updated_at'].strftime('%Y-%m-%d') if doc['updated_at'] else 'unknown'}"
        )
        if doc["page_count"]:
            header += f" | Pages: {doc['page_count']}"
        header += f"\n[View in Supavault]({link})\n\n---\n\n"

        image_types = {"png", "jpg", "jpeg", "webp", "gif"}
        if file_type in image_types:
            if not include_images:
                return header + "(Image file 鈥?set `include_images=true` to view)"
            s3_key = f"{doc['user_id']}/{doc['id']}/source.{file_type}"
            img_bytes = await load_s3_bytes(s3_key)
            if img_bytes:
                fmt = "jpeg" if file_type in ("jpg", "jpeg") else file_type
                return [_text(header), _image(img_bytes, fmt)]
            return header + "(Image could not be loaded from storage)"

        has_pages = file_type in ("pdf", "pptx", "ppt", "docx", "doc", "xlsx", "xls", "csv")
        spreadsheet_types = {"xlsx", "xls", "csv"}

        if has_pages and pages:
            return await _read_pages(doc, kb, header, pages, include_images)

        if file_type in spreadsheet_types and not pages:
            return await _read_spreadsheet_index(doc, header)

        content = doc["content"] or ""
        if sections:
            content = _extract_sections(content, sections)

        return header + content


Get-Content mcp\tools\write.py -TotalCount 360

import re
from datetime import date
from typing import Literal

from mcp.server.fastmcp import FastMCP, Context

from db import scoped_queryrow, service_queryrow, service_execute
from .helpers import get_user_id, resolve_kb, deep_link, resolve_path

_ASSET_EXTENSIONS = {".svg", ".csv", ".json", ".xml", ".html"}

async def _create_note(
    user_id: str, kb: dict, path: str, title: str, content: str,
    tags: list[str], date_str: str,
) -> str:
    if not title:
        return "Error: title is required when creating a note."
    if not tags:
        return "Error: at least one tag is required when creating a note."

    dir_path = path if path.endswith("/") else path + "/"
    if not dir_path.startswith("/"):
        dir_path = "/" + dir_path

    # Detect asset extensions
    _title_lower = title.lower()
    asset_ext = None
    for ext in _ASSET_EXTENSIONS:
        if _title_lower.endswith(ext):
            asset_ext = ext
            break

    # Derive filename (slug) from title
    if asset_ext:
        filename = re.sub(r"[^\w\s\-.]", "", _title_lower.replace(" ", "-"))
        file_type = asset_ext.lstrip(".")
    else:
        slug = _title_lower
        # Strip .md if Claude passed a filename as the title
        slug = re.sub(r"\.(md|txt)$", "", slug)
        filename = re.sub(r"[^\w\s\-.]", "", slug.replace(" ", "-"))
        if not filename.endswith(".md"):
            filename += ".md"
        file_type = "md"

    # Ensure title is human-readable, not a slug
    # "operating-leverage.md" 鈫?"Operating Leverage"
    clean_title = re.sub(r"\.(md|txt|svg|csv|json|xml|html)$", "", title)
    if clean_title == clean_title.lower() and "-" in clean_title:
        clean_title = clean_title.replace("-", " ").replace("_", " ").strip().title()
    title = clean_title

    note_date = date_str or date.today().isoformat()

    doc = await service_queryrow(
        "INSERT INTO documents (knowledge_base_id, user_id, filename, title, path, "
        "file_type, status, content, tags, version) "
        "VALUES ($1, $2, $3, $4, $5, $6, 'ready', $7, $8, 0) "
        "RETURNING id, filename, path",
        kb["id"], user_id, filename, title, dir_path, file_type, content, tags,
    )

    link = deep_link(kb["slug"], doc["path"], doc["filename"])

    is_wiki = dir_path.startswith("/wiki/")
    suffix = ""
    if asset_ext:
        suffix = f"\n\nEmbed in wiki pages with: `![{title}]({filename})`"
    elif is_wiki:
        suffix = "\n\nRemember to cite sources using footnotes: `[^1]: source-file.pdf, p.X`"

    return (
        f"Created **{title}** at `{dir_path}{filename}`\n"
        f"Tags: {', '.join(tags)} | Date: {note_date}\n"
        f"[View in Supavault]({link}){suffix}"
    )

async def _edit_note(user_id: str, kb: dict, path: str, old_text: str, new_text: str) -> str:
    if not old_text:
        return "Error: old_text is required for str_replace."

    dir_path, filename = resolve_path(path)

    doc = await scoped_queryrow(
        user_id,
        "SELECT id, content FROM documents "
        "WHERE knowledge_base_id = $1 AND filename = $2 AND path = $3 AND NOT archived",
        kb["id"], filename, dir_path,
    )
    if not doc:
        return f"Document '{path}' not found."

    content = doc["content"] or ""
    count = content.count(old_text)
    if count == 0:
        return "Error: no match found for old_text."
    if count > 1:
        return f"Error: found {count} matches for old_text. Provide more context to match exactly once."

    new_content = content.replace(old_text, new_text, 1)
    await service_execute(
        "UPDATE documents SET content = $1, version = version + 1 "
        "WHERE id = $2 AND user_id = $3",
        new_content, doc["id"], user_id,
    )

    link = deep_link(kb["slug"], dir_path, filename)
    return f"Edited `{path}`. Replaced 1 occurrence.\n[View in Supavault]({link})"

async def _append_note(user_id: str, kb: dict, path: str, content: str) -> str:
    dir_path, filename = resolve_path(path)

    doc = await scoped_queryrow(
        user_id,
        "SELECT id, content FROM documents "
        "WHERE knowledge_base_id = $1 AND filename = $2 AND path = $3 AND NOT archived",
        kb["id"], filename, dir_path,
    )
    if not doc:
        return f"Document '{path}' not found."

    new_content = (doc["content"] or "") + "\n\n" + content
    await service_execute(
        "UPDATE documents SET content = $1, version = version + 1 "
        "WHERE id = $2 AND user_id = $3",
        new_content, doc["id"], user_id,
    )

    link = deep_link(kb["slug"], dir_path, filename)
    return f"Appended to `{path}`.\n[View in Supavault]({link})"

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="write",
        description=(
            "Create or edit notes and wiki pages in the knowledge vault.\n\n"
            "Wiki pages should be created under `/wiki/` and should cite their sources using "
            "markdown footnotes (e.g. `[^1]: paper.pdf, p.3`).\n\n"
            "You can also create SVG diagrams and CSV data files as wiki assets:\n"
            "- `write(command=\"create\", path=\"/wiki/\", title=\"architecture-diagram.svg\", content=\"<svg>...</svg>\", tags=[\"diagram\"])`\n"
            "- `write(command=\"create\", path=\"/wiki/\", title=\"data-table.csv\", content=\"col1,col2\\nval1,val2\", tags=[\"data\"])`\n"
            "SVGs and other assets can be embedded in wiki pages via `![Architecture](architecture-diagram.svg)`\n\n"
            "Commands:\n"
            "- create: create a new page (title and tags are REQUIRED)\n"
            "- str_replace: replace exact text in an existing page (read first)\n"
            "- append: add content to the end of an existing page"
        ),
    )
    async def write(
        ctx: Context,
        knowledge_base: str,
        command: Literal["create", "str_replace", "append"],
        path: str = "/",
        title: str = "",
        content: str = "",
        tags: list[str] | None = None,
        date_str: str = "",
        old_text: str = "",
        new_text: str = "",
    ) -> str:
        user_id = get_user_id(ctx)

        kb = await resolve_kb(user_id, knowledge_base)
        if not kb:
            return f"Knowledge base '{knowledge_base}' not found."

        if command == "create":
            return await _create_note(user_id, kb, path, title, content, tags or [], date_str)
        elif command == "str_replace":
            return await _edit_note(user_id, kb, path, old_text, new_text)
        elif command == "append":
            return await _append_note(user_id, kb, path, content)

        return f"Unknown command: {command}"


Output:
Exit code: 0
Wall time: 0.8 seconds
Output:
from mcp.server.fastmcp import FastMCP, Context

from config import settings
from db import scoped_query
from .helpers import get_user_id

GUIDE_TEXT = """# LLM Wiki 鈥?How It Works

榛樿杈撳嚭璇█锛氫腑鏂囷紙绠€浣擄級銆傞櫎闈炵敤鎴锋槑纭姹傚叾浠栬瑷€锛屽惁鍒欒浣跨敤涓枃瀹屾垚闂瓟涓?Wiki 椤甸潰鎾板啓銆?
You are connected to an **LLM Wiki** 鈥?a personal knowledge workspace where you compile and maintain a structured wiki from raw source documents.

## Architecture

1. **Raw Sources** (path: `/`) 鈥?uploaded documents (PDFs, notes, images, spreadsheets). Source of truth. Read-only.
2. **Compiled Wiki** (path: `/wiki/`) 鈥?markdown pages YOU create and maintain. You own this layer.
3. **Tools** 鈥?`search`, `read`, `write`, `delete` 鈥?your interface to both layers.

## Wiki Structure

Every wiki follows this structure. These categories are not suggestions 鈥?they are the backbone of the wiki.

### Overview (`/wiki/overview.md`) 鈥?THE HUB PAGE
Always exists. This is the front page of the wiki. It must contain:
- A summary of what this wiki covers and its scope
- **Source count** and page count (update on every ingest)
- **Key Findings** 鈥?the most important insights across all sources
- **Recent Updates** 鈥?last 5-10 actions (ingests, new pages, revisions)

Update the Overview after EVERY ingest or major edit. If you only update one page, it should be this one.

### Concepts (`/wiki/concepts/`) 鈥?ABSTRACT IDEAS
Pages for theoretical frameworks, methodologies, principles, themes 鈥?anything conceptual.
- `/wiki/concepts/scaling-laws.md`
- `/wiki/concepts/attention-mechanisms.md`
- `/wiki/concepts/self-supervised-learning.md`

Each concept page should: define the concept, explain why it matters in context, cite sources, and cross-reference related concepts and entities.

### Entities (`/wiki/entities/`) 鈥?CONCRETE THINGS
Pages for people, organizations, products, technologies, papers, datasets 鈥?anything you can point to.
- `/wiki/entities/transformer.md`
- `/wiki/entities/openai.md`
- `/wiki/entities/attention-is-all-you-need.md`

Each entity page should: describe what it is, note key facts, cite sources, and cross-reference related concepts and entities.

### Log (`/wiki/log.md`) 鈥?CHRONOLOGICAL RECORD
Always exists. Append-only. Records every ingest, major edit, and lint pass. Never delete entries.

Format 鈥?each entry starts with a parseable header:
```
## [YYYY-MM-DD] ingest | Source Title
- Created concept page: [Page Title](concepts/page.md)
- Updated entity page: [Page Title](entities/page.md)
- Updated overview with new findings
- Key takeaway: one sentence summary

## [YYYY-MM-DD] query | Question Asked
- Created new page: [Page Title](concepts/page.md)
- Finding: one sentence answer

## [YYYY-MM-DD] lint | Health Check
- Fixed contradiction between X and Y
- Added missing cross-reference in Z
```

### Additional Pages
You can create pages outside of concepts/ and entities/ when needed:
- `/wiki/comparisons/x-vs-y.md` 鈥?for deep comparisons
- `/wiki/timeline.md` 鈥?for chronological narratives

But concepts/ and entities/ are the primary categories. When in doubt, file there.

## Page Hierarchy

Wiki pages use a parent/child hierarchy via paths:
- `/wiki/concepts.md` 鈥?parent page (optional; summarizes all concepts)
- `/wiki/concepts/attention.md` 鈥?child page

Parent pages summarize; child pages go deep. The UI renders this as an expandable tree.

## Writing Standards

**Wiki pages must be substantially richer than a chat response.** They are persistent, curated artifacts.

### Structure
- Start with a summary paragraph (no H1 鈥?the title is rendered by the UI)
- Use `##` for major sections, `###` for subsections
- One idea per section. Bullet points for facts, prose for synthesis.

### Visual Elements 鈥?MANDATORY

**Every wiki page MUST include at least one visual element.** A page with only prose is incomplete.

**Mermaid diagrams** 鈥?use for ANY structured relationship:
- Flowcharts for processes, pipelines, decision trees
- Sequence diagrams for interactions, timelines
- Quadrant charts for comparisons, trade-off analyses
- Entity relationship diagrams for people, companies, concepts

````
```mermaid
graph LR
    A[Input] --> B[Process] --> C[Output]
```
````

**Tables** 鈥?use for ANY structured comparison:
- Feature matrices, pros/cons, timelines, metrics
- If you're listing 3+ items with attributes, it should be a table

**SVG assets** 鈥?for custom visuals Mermaid can't express:
- Create: `write(command="create", path="/wiki/", title="diagram.svg", content="<svg>...</svg>", tags=["diagram"])`
- Embed in wiki pages: `![Description](diagram.svg)`

### Citations 鈥?REQUIRED

Every factual claim MUST cite its source via markdown footnotes:
```
Transformers use self-attention[^1] that scales quadratically[^2].

[^1]: attention-paper.pdf, p.3
[^2]: scaling-laws.pdf, p.12-14
```

Rules:
- Use the FULL source filename 鈥?never truncate
- Add page numbers for PDFs: `paper.pdf, p.3`
- One citation per claim 鈥?don't batch unrelated claims
- Citations render as hoverable popover badges in the UI

### Cross-References
Link between wiki pages using standard markdown links to other wiki paths.

## Core Workflows

### Ingest a New Source
1. Read it: `read(path="source.pdf", pages="1-10")`
2. Discuss key takeaways with the user
3. Create or update **concept** pages under `/wiki/concepts/`
4. Create or update **entity** pages under `/wiki/entities/`
5. Update `/wiki/overview.md` 鈥?source count, key findings, recent updates
6. Append an entry to `/wiki/log.md`
7. A single source typically touches 5-15 wiki pages 鈥?that's expected

### Answer a Question
1. `search(mode="search", query="term")` to find relevant content
2. Read relevant wiki pages and sources
3. Synthesize with citations
4. If the answer is valuable, file it as a new wiki page 鈥?explorations should compound
5. Append a query entry to `/wiki/log.md`

### Maintain the Wiki (Lint)
Check for: contradictions, orphan pages, missing cross-references, stale claims, concepts mentioned but lacking their own page. Append a lint entry to `/wiki/log.md`.

## Available Knowledge Bases

"""

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="guide",
        description="Get started with LLM Wiki. Call this to understand how the knowledge vault works and see your available knowledge bases.",
    )
    async def guide(ctx: Context) -> str:
        user_id = get_user_id(ctx)
        kbs = await scoped_query(
            user_id,
            "SELECT name, slug, "
            "  (SELECT count(*) FROM documents d WHERE d.knowledge_base_id = kb.id AND d.path NOT LIKE '/wiki/%%' AND NOT d.archived) as source_count, "
            "  (SELECT count(*) FROM documents d WHERE d.knowledge_base_id = kb.id AND d.path LIKE '/wiki/%%' AND NOT d.archived) as wiki_count "
            "FROM knowledge_bases kb ORDER BY created_at DESC",
        )
        if not kbs:
            return GUIDE_TEXT + "No knowledge bases yet. Create one at " + settings.APP_URL + "/wikis"

        lines = []
        for kb in kbs:
            lines.append(f"- **{kb['name']}** (`{kb['slug']}`) 鈥?{kb['source_count']} sources, {kb['wiki_count']} wiki pages")
        return GUIDE_TEXT + "\n".join(lines)


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
import logging
from typing import Literal

from mcp.server.fastmcp import FastMCP, Context

from db import scoped_query, scoped_queryrow
from .helpers import get_user_id, resolve_kb, deep_link, glob_match, MAX_LIST, MAX_SEARCH

logger = logging.getLogger(__name__)

CONTEXT_CHARS = 120

def _extract_snippet(content: str, query: str) -> str:
    if not content:
        return "(empty)"
    idx = content.lower().find(query.lower())
    if idx < 0:
        return content[:CONTEXT_CHARS * 2].strip()
    start = max(0, idx - CONTEXT_CHARS)
    end = min(len(content), idx + len(query) + CONTEXT_CHARS)
    snippet = content[start:end].strip()
    if start > 0:
        snippet = "..." + snippet
    if end < len(content):
        snippet = snippet + "..."
    return snippet

async def _list_all_kbs(user_id: str) -> str:
    kbs = await scoped_query(
        user_id,
        "SELECT name, slug, created_at FROM knowledge_bases ORDER BY created_at DESC",
    )
    if not kbs:
        return "No knowledge bases found. Create one first."

    lines = ["**Knowledge Bases:**\n"]
    for kb in kbs:
        doc_count = await scoped_queryrow(
            user_id,
            "SELECT count(*) as cnt FROM documents WHERE knowledge_base_id = ("
            "SELECT id FROM knowledge_bases WHERE slug = $1) AND NOT archived",
            kb["slug"],
        )
        cnt = doc_count["cnt"] if doc_count else 0
        lines.append(f"  {kb['slug']}/ 鈥?{kb['name']} ({cnt} documents)")
    return "\n".join(lines)

async def _list_documents(user_id: str, kb: dict, target: str, tags: list[str] | None) -> str:
    docs = await scoped_query(
        user_id,
        "SELECT id, filename, title, path, file_type, tags, page_count, updated_at "
        "FROM documents WHERE knowledge_base_id = $1 AND NOT archived "
        "ORDER BY path, filename",
        kb["id"],
    )

    if target not in ("*", "**", "**/*"):
        glob_pat = "/" + target.lstrip("/") if not target.startswith("/") else target
        docs = [d for d in docs if glob_match(d["path"] + d["filename"], glob_pat)]

    if tags:
        tag_set = {t.lower() for t in tags}
        docs = [d for d in docs if tag_set.issubset({t.lower() for t in (d["tags"] or [])})]

    if not docs:
        return f"No matches for `{target}` in {kb['slug']}."

    sources = [d for d in docs if not d["path"].startswith("/wiki/")]
    wiki_pages = [d for d in docs if d["path"].startswith("/wiki/")]

    lines = [f"**{kb['name']}** (`{target}`):\n"]

    if sources:
        lines.append(f"**Sources ({len(sources)}):**")
        for doc in sources[:MAX_LIST]:
            tag_str = f" [{', '.join(doc['tags'])}]" if doc["tags"] else ""
            date_part = f", {doc['updated_at'].strftime('%Y-%m-%d')}" if doc["updated_at"] else ""
            pages_part = f", {doc['page_count']}p" if doc["page_count"] else ""
            lines.append(f"  {doc['path']}{doc['filename']} ({doc['file_type']}{pages_part}{date_part}){tag_str}")
        if len(sources) > MAX_LIST:
            lines.append(f"  ... {len(sources) - MAX_LIST} more")

    if wiki_pages:
        if sources:
            lines.append("")
        lines.append(f"**Wiki ({len(wiki_pages)} pages):**")
        for doc in wiki_pages[:MAX_LIST]:
            date_part = f", {doc['updated_at'].strftime('%Y-%m-%d')}" if doc["updated_at"] else ""
            lines.append(f"  {doc['path']}{doc['filename']}{date_part}")

    return "\n".join(lines)

async def _search_chunks(
    user_id: str, kb: dict, query: str, path: str,
    tags: list[str] | None, limit: int,
) -> str:
    path_filter = ""
    if path not in ("*", "**", "**/*"):
        if path.startswith("/wiki"):
            path_filter = " AND d.path LIKE '/wiki/%%'"
        elif path == "/" or path == "/*":
            path_filter = " AND d.path NOT LIKE '/wiki/%%'"

    matches = await scoped_query(
        user_id,
        f"SELECT dc.content, dc.page, dc.header_breadcrumb, dc.chunk_index, "
        f"  d.filename, d.title, d.path, d.file_type, d.tags, "
        f"  pgroonga_score(dc.tableoid, dc.ctid) AS score "
        f"FROM document_chunks dc "
        f"JOIN documents d ON dc.document_id = d.id "
        f"WHERE dc.knowledge_base_id = $1 "
        f"  AND dc.content &@~ $2 "
        f"  AND NOT d.archived"
        f"{path_filter} "
        f"ORDER BY score DESC, dc.chunk_index "
        f"LIMIT {limit}",
        kb["id"], query,
    )

    if tags:
        tag_set = {t.lower() for t in tags}
        matches = [m for m in matches if tag_set.issubset({t.lower() for t in (m.get("tags") or [])})]

    if not matches:
        return f"No matches for `{query}` in {kb['slug']}."

    lines = [f"**{len(matches)} result(s)** for `{query}`:\n"]
    for m in matches:
        filepath = f"{m['path']}{m['filename']}"
        page_str = f" (p.{m['page']})" if m['page'] else ""
        breadcrumb = f"\n  {m['header_breadcrumb']}" if m["header_breadcrumb"] else ""
        snippet = _extract_snippet(m["content"], query)
        link = deep_link(kb["slug"], m["path"], m["filename"])
        score = m.get("score", 0)
        score_str = f" [{score:.1f}]" if score else ""
        lines.append(f"**{filepath}**{page_str}{score_str} 鈥?[view]({link}){breadcrumb}")
        lines.append(f"```\n{snippet}\n```\n")

    return "\n".join(lines)

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="search",
        description=(
            "Browse or search the knowledge vault.\n\n"
            "Sources (raw documents) live at `/`. Wiki pages (LLM-compiled) live at `/wiki/`.\n\n"
            "Modes:\n"
            "- list: browse files and folders\n"
            "- search: keyword search across document content (searches chunks for precise results with page numbers)\n\n"
            "Use `path` to scope: `*` for root, `/wiki/**` for wiki only, `*.pdf` for PDFs, etc.\n"
            "Use `tags` to filter by document tags."
        ),
    )
    async def search(
        ctx: Context,
        knowledge_base: str,
        mode: Literal["list", "search"] = "list",
        query: str = "",
        path: str = "*",
        tags: list[str] | None = None,
        limit: int = 10,
    ) -> str:
        user_id = get_user_id(ctx)

        if not knowledge_base:
            return await _list_all_kbs(user_id)

        kb = await resolve_kb(user_id, knowledge_base)
        if not kb:
            return f"Knowledge base '{knowledge_base}' not found."

        if mode == "list":
            return await _list_documents(user_id, kb, path, tags)
        elif mode == "search":
            if not query:
                return "search mode requires a query."
            return await _search_chunks(user_id, kb, query, path, tags, min(limit, MAX_SEARCH))

        return f"Unknown mode: {mode}"


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
import base64
import json
import logging

from mcp.server.fastmcp import FastMCP, Context
from mcp.types import TextContent, ImageContent

from db import scoped_query, scoped_queryrow
from .helpers import (
    get_user_id, resolve_kb, deep_link, resolve_path,
    load_s3_bytes, parse_page_range, glob_match,
)

logger = logging.getLogger(__name__)

MAX_BATCH_CHARS = 120_000

_IMG_MIME = {
    "png": "image/png",
    "jpg": "image/jpeg",
    "jpeg": "image/jpeg",
    "webp": "image/webp",
    "gif": "image/gif",
}

def _text(s: str) -> TextContent:
    return TextContent(type="text", text=s)

def _image(data: bytes, fmt: str) -> ImageContent:
    return ImageContent(
        type="image",
        data=base64.b64encode(data).decode(),
        mimeType=_IMG_MIME.get(fmt, f"image/{fmt}"),
    )

def _extract_sections(content: str, section_names: list[str]) -> str:
    lines = content.split("\n")
    sections = []
    current_section = None
    current_lines = []

    for line in lines:
        if line.startswith("#"):
            if current_section and current_lines:
                sections.append((current_section, "\n".join(current_lines)))
            heading = line.lstrip("#").strip()
            current_section = heading
            current_lines = [line]
        elif current_section:
            current_lines.append(line)

    if current_section and current_lines:
        sections.append((current_section, "\n".join(current_lines)))

    wanted = {s.lower() for s in section_names}
    matched = [text for name, text in sections if name.lower() in wanted]

    if not matched:
        return f"No sections matching {section_names} found."
    return "\n\n".join(matched)

async def _read_pages(
    doc: dict, kb: dict, header: str, pages_str: str, include_images: bool = False,
) -> str | list:
    max_page = doc["page_count"] or 1
    page_nums = parse_page_range(pages_str, max_page)
    if not page_nums:
        return header + f"Invalid page range: {pages_str} (document has {max_page} pages)"

    user_id = str(doc["user_id"])
    doc_id = str(doc["id"])

    page_rows = await scoped_query(
        user_id,
        "SELECT page, content, elements FROM document_pages "
        "WHERE document_id = $1 AND page = ANY($2) ORDER BY page",
        doc["id"], page_nums,
    )

    if not page_rows:
        return header + f"No page data found for pages {pages_str}."

    content_blocks: list[TextContent | ImageContent] = [_text(header)]
    has_images = False

    for row in page_rows:
        content_blocks.append(_text(f"**鈥?Page {row['page']} 鈥?*\n\n{row['content']}"))

        if not include_images:
            continue

        elements = row["elements"]
        if not elements:
            continue
        if isinstance(elements, str):
            elements = json.loads(elements)

        images = elements.get("images", [])
        for img_meta in images:
            img_id = img_meta.get("id")
            if not img_id:
                continue
            s3_key = f"{user_id}/{doc_id}/images/{img_id}"
            img_bytes = await load_s3_bytes(s3_key)
            if img_bytes:
                fmt = "jpeg" if img_id.endswith((".jpg", ".jpeg")) else "png"
                content_blocks.append(_image(img_bytes, fmt))
                has_images = True

    if has_images:
        return content_blocks
    return "\n\n".join(b.text for b in content_blocks)

async def _read_spreadsheet_index(doc: dict, header: str) -> str:
    user_id = str(doc["user_id"])
    page_rows = await scoped_query(
        user_id,
        "SELECT page, content, elements FROM document_pages "
        "WHERE document_id = $1 ORDER BY page",
        doc["id"],
    )
    if not page_rows:
        return header + (doc["content"] or "(no data)")

    lines = [header, "**Sheets:**\n"]
    for row in page_rows:
        elements = row["elements"]
        if isinstance(elements, str):
            elements = json.loads(elements)
        sheet_name = (elements or {}).get("sheet_name", f"Sheet {row['page']}")
        row_count = row["content"].count("\n") if row["content"] else 0
        lines.append(f"  Page {row['page']}: **{sheet_name}** (~{row_count} rows)")
    lines.append(f"\nUse `pages=\"1\"` to read a specific sheet.")
    return "\n".join(lines)

async def _read_batch(user_id: str, kb: dict, path: str) -> str:
    docs = await scoped_query(
        user_id,
        "SELECT id, filename, title, path, content, tags, file_type, page_count "
        "FROM documents WHERE knowledge_base_id = $1 AND NOT archived "
        "ORDER BY path, filename",
        kb["id"],
    )

    glob_pat = "/" + path.lstrip("/") if not path.startswith("/") else path
    docs = [d for d in docs if glob_match(d["path"] + d["filename"], glob_pat)]

    if not docs:
        return f"No documents matching `{path}` in {kb['slug']}."

    text_types = {"md", "txt", "csv", "html", "svg", "json", "xml"}
    parts = []
    chars_used = 0
    truncated_docs = 0
    skipped_docs = []

    for doc in docs:
        if chars_used >= MAX_BATCH_CHARS:
            skipped_docs.append(doc)
            continue

        link = deep_link(kb["slug"], doc["path"], doc["filename"])
        ft = doc["file_type"] or ""
        remaining = MAX_BATCH_CHARS - chars_used

        if ft in text_types and doc["content"]:
            content = doc["content"] or ""
            if len(content) > remaining:
                content = content[:remaining] + "\n\n... (truncated)"
                truncated_docs += 1
            parts.append(f"### [{doc['path']}{doc['filename']}]({link})\n\n{content}")
            chars_used += len(content)

        elif (doc.get("page_count") or 0) > 0:
            page_rows = await scoped_query(
                user_id,
                "SELECT page, content FROM document_pages "
                "WHERE document_id = $1 ORDER BY page",
                doc["id"],
            )
            page_parts = []
            doc_chars = 0
            pages_included = 0
            for r in page_rows:
                page_text = f"**鈥?Page {r['page']} 鈥?*\n\n{r['content']}"
                if doc_chars + len(page_text) > remaining:
                    page_parts.append(page_text[:remaining - doc_chars] + "\n\n... (truncated)")
                    truncated_docs += 1
                    pages_included += 1
                    doc_chars = remaining
                    break
                page_parts.append(page_text)
                doc_chars += len(page_text)
                pages_included += 1

            total_pages = doc["page_count"]
            remaining_pages = total_pages - pages_included
            suffix = ""
            if remaining_pages > 0:
                suffix = f"\n\n*({remaining_pages} more pages 鈥?use `pages=\"{pages_included+1}-{total_pages}\"` to continue)*"
            parts.append(f"### [{doc['path']}{doc['filename']}]({link}) ({total_pages} pages)\n\n" + "\n\n".join(page_parts) + suffix)
            chars_used += doc_chars

        else:
            skipped_docs.append(doc)

    header = f"**{len(parts)} document(s)** matching `{path}`"
    if truncated_docs:
        header += f" (some truncated to fit {MAX_BATCH_CHARS:,} char budget)"
    if skipped_docs:
        header += f"\n*{len(skipped_docs)} more document(s) beyond budget 鈥?read individually*"
    header += "\n\n---\n\n"

    return header + "\n\n---\n\n".join(parts)

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="read",
        description=(
            "Read document content from the knowledge vault.\n\n"
            "Accepts a single file path OR a glob pattern to batch-read multiple files:\n"
            "- `path=\"notes.md\"` 鈥?read one file\n"
            "- `path=\"*.md\"` 鈥?read all markdown files in root\n"
            "- `path=\"/wiki/**\"` 鈥?read all wiki pages\n"
            "- `path=\"**/*.md\"` 鈥?read all markdown files everywhere\n\n"
            "Batch reads are the PREFERRED way to read multiple documents at once 鈥?use them generously.\n"
            "Glob reads sample the first few pages from each document (including PDFs) up to a 120k char budget. "
            "This gives you a broad overview of an entire folder in one call. Read individual files for full content.\n\n"
            "For PDFs and office docs, use `pages` to read specific page ranges (e.g. '1-50', '3', '10-30').\n"
            "You can read up to 50+ pages in a single call 鈥?use wide ranges to avoid unnecessary round trips.\n"
            "For spreadsheets, each sheet is a page (call without pages first to see sheet names).\n"
            "Set `include_images=true` to include embedded images (e.g. figures in PDFs, standalone image files). "
            "Off by default to save context 鈥?enable when you need to see charts, diagrams, or photos.\n\n"
            "When reading sources to compile wiki pages, note the filename and page ranges for citation."
        ),
        structured_output=False,
    )
    async def read(
        ctx: Context,
        knowledge_base: str,
        path: str,
        pages: str = "",
        sections: list[str] | None = None,
        include_images: bool = False,
    ) -> str | list:
        user_id = get_user_id(ctx)

        kb = await resolve_kb(user_id, knowledge_base)
        if not kb:
            return f"Knowledge base '{knowledge_base}' not found."

        is_glob = "*" in path or "?" in path
        if is_glob:
            return await _read_batch(user_id, kb, path)

        dir_path, filename = resolve_path(path)

        doc = await scoped_queryrow(
            user_id,
            "SELECT id, user_id, filename, title, path, content, tags, version, file_type, "
            "page_count, created_at, updated_at "
            "FROM documents WHERE knowledge_base_id = $1 AND filename = $2 AND path = $3 AND NOT archived",
            kb["id"], filename, dir_path,
        )
        if not doc:
            doc = await scoped_queryrow(
                user_id,
                "SELECT id, user_id, filename, title, path, content, tags, version, file_type, "
                "page_count, created_at, updated_at "
                "FROM documents WHERE knowledge_base_id = $1 AND (filename = $2 OR title = $2) AND NOT archived",
                kb["id"], path.lstrip("/").split("/")[-1],
            )

        if not doc:
            return f"Document '{path}' not found in {knowledge_base}."

        tags_str = ", ".join(doc["tags"]) if doc["tags"] else "none"
        link = deep_link(kb["slug"], doc["path"], doc["filename"])
        file_type = doc["file_type"] or ""

        header = (
            f"**{doc['title'] or doc['filename']}**\n"
            f"Type: {file_type} | Tags: {tags_str} | Version: {doc['version']} | "
            f"Updated: {doc['updated_at'].strftime('%Y-%m-%d') if doc['updated_at'] else 'unknown'}"
        )
        if doc["page_count"]:
            header += f" | Pages: {doc['page_count']}"
        header += f"\n[View in Supavault]({link})\n\n---\n\n"

        image_types = {"png", "jpg", "jpeg", "webp", "gif"}
        if file_type in image_types:
            if not include_images:
                return header + "(Image file 鈥?set `include_images=true` to view)"
            s3_key = f"{doc['user_id']}/{doc['id']}/source.{file_type}"
            img_bytes = await load_s3_bytes(s3_key)
            if img_bytes:
                fmt = "jpeg" if file_type in ("jpg", "jpeg") else file_type
                return [_text(header), _image(img_bytes, fmt)]
            return header + "(Image could not be loaded from storage)"

        has_pages = file_type in ("pdf", "pptx", "ppt", "docx", "doc", "xlsx", "xls", "csv")
        spreadsheet_types = {"xlsx", "xls", "csv"}

        if has_pages and pages:
            return await _read_pages(doc, kb, header, pages, include_images)

        if file_type in spreadsheet_types and not pages:
            return await _read_spreadsheet_index(doc, header)

        content = doc["content"] or ""
        if sections:
            content = _extract_sections(content, sections)

        return header + content


Output:
Exit code: 0
Wall time: 0.8 seconds
Output:
import re
from datetime import date
from typing import Literal

from mcp.server.fastmcp import FastMCP, Context

from db import scoped_queryrow, service_queryrow, service_execute
from .helpers import get_user_id, resolve_kb, deep_link, resolve_path

_ASSET_EXTENSIONS = {".svg", ".csv", ".json", ".xml", ".html"}

async def _create_note(
    user_id: str, kb: dict, path: str, title: str, content: str,
    tags: list[str], date_str: str,
) -> str:
    if not title:
        return "Error: title is required when creating a note."
    if not tags:
        return "Error: at least one tag is required when creating a note."

    dir_path = path if path.endswith("/") else path + "/"
    if not dir_path.startswith("/"):
        dir_path = "/" + dir_path

    # Detect asset extensions
    _title_lower = title.lower()
    asset_ext = None
    for ext in _ASSET_EXTENSIONS:
        if _title_lower.endswith(ext):
            asset_ext = ext
            break

    # Derive filename (slug) from title
    if asset_ext:
        filename = re.sub(r"[^\w\s\-.]", "", _title_lower.replace(" ", "-"))
        file_type = asset_ext.lstrip(".")
    else:
        slug = _title_lower
        # Strip .md if Claude passed a filename as the title
        slug = re.sub(r"\.(md|txt)$", "", slug)
        filename = re.sub(r"[^\w\s\-.]", "", slug.replace(" ", "-"))
        if not filename.endswith(".md"):
            filename += ".md"
        file_type = "md"

    # Ensure title is human-readable, not a slug
    # "operating-leverage.md" 鈫?"Operating Leverage"
    clean_title = re.sub(r"\.(md|txt|svg|csv|json|xml|html)$", "", title)
    if clean_title == clean_title.lower() and "-" in clean_title:
        clean_title = clean_title.replace("-", " ").replace("_", " ").strip().title()
    title = clean_title

    note_date = date_str or date.today().isoformat()

    doc = await service_queryrow(
        "INSERT INTO documents (knowledge_base_id, user_id, filename, title, path, "
        "file_type, status, content, tags, version) "
        "VALUES ($1, $2, $3, $4, $5, $6, 'ready', $7, $8, 0) "
        "RETURNING id, filename, path",
        kb["id"], user_id, filename, title, dir_path, file_type, content, tags,
    )

    link = deep_link(kb["slug"], doc["path"], doc["filename"])

    is_wiki = dir_path.startswith("/wiki/")
    suffix = ""
    if asset_ext:
        suffix = f"\n\nEmbed in wiki pages with: `![{title}]({filename})`"
    elif is_wiki:
        suffix = "\n\nRemember to cite sources using footnotes: `[^1]: source-file.pdf, p.X`"

    return (
        f"Created **{title}** at `{dir_path}{filename}`\n"
        f"Tags: {', '.join(tags)} | Date: {note_date}\n"
        f"[View in Supavault]({link}){suffix}"
    )

async def _edit_note(user_id: str, kb: dict, path: str, old_text: str, new_text: str) -> str:
    if not old_text:
        return "Error: old_text is required for str_replace."

    dir_path, filename = resolve_path(path)

    doc = await scoped_queryrow(
        user_id,
        "SELECT id, content FROM documents "
        "WHERE knowledge_base_id = $1 AND filename = $2 AND path = $3 AND NOT archived",
        kb["id"], filename, dir_path,
    )
    if not doc:
        return f"Document '{path}' not found."

    content = doc["content"] or ""
    count = content.count(old_text)
    if count == 0:
        return "Error: no match found for old_text."
    if count > 1:
        return f"Error: found {count} matches for old_text. Provide more context to match exactly once."

    new_content = content.replace(old_text, new_text, 1)
    await service_execute(
        "UPDATE documents SET content = $1, version = version + 1 "
        "WHERE id = $2 AND user_id = $3",
        new_content, doc["id"], user_id,
    )

    link = deep_link(kb["slug"], dir_path, filename)
    return f"Edited `{path}`. Replaced 1 occurrence.\n[View in Supavault]({link})"

async def _append_note(user_id: str, kb: dict, path: str, content: str) -> str:
    dir_path, filename = resolve_path(path)

    doc = await scoped_queryrow(
        user_id,
        "SELECT id, content FROM documents "
        "WHERE knowledge_base_id = $1 AND filename = $2 AND path = $3 AND NOT archived",
        kb["id"], filename, dir_path,
    )
    if not doc:
        return f"Document '{path}' not found."

    new_content = (doc["content"] or "") + "\n\n" + content
    await service_execute(
        "UPDATE documents SET content = $1, version = version + 1 "
        "WHERE id = $2 AND user_id = $3",
        new_content, doc["id"], user_id,
    )

    link = deep_link(kb["slug"], dir_path, filename)
    return f"Appended to `{path}`.\n[View in Supavault]({link})"

def register(mcp: FastMCP) -> None:

    @mcp.tool(
        name="write",
        description=(
            "Create or edit notes and wiki pages in the knowledge vault.\n\n"
            "Wiki pages should be created under `/wiki/` and should cite their sources using "
            "markdown footnotes (e.g. `[^1]: paper.pdf, p.3`).\n\n"
            "You can also create SVG diagrams and CSV data files as wiki assets:\n"
            "- `write(command=\"create\", path=\"/wiki/\", title=\"architecture-diagram.svg\", content=\"<svg>...</svg>\", tags=[\"diagram\"])`\n"
            "- `write(command=\"create\", path=\"/wiki/\", title=\"data-table.csv\", content=\"col1,col2\\nval1,val2\", tags=[\"data\"])`\n"
            "SVGs and other assets can be embedded in wiki pages via `![Architecture](architecture-diagram.svg)`\n\n"
            "Commands:\n"
            "- create: create a new page (title and tags are REQUIRED)\n"
            "- str_replace: replace exact text in an existing page (read first)\n"
            "- append: add content to the end of an existing page"
        ),
    )
    async def write(
        ctx: Context,
        knowledge_base: str,
        command: Literal["create", "str_replace", "append"],
        path: str = "/",
        title: str = "",
        content: str = "",
        tags: list[str] | None = None,
        date_str: str = "",
        old_text: str = "",
        new_text: str = "",
    ) -> str:
        user_id = get_user_id(ctx)

        kb = await resolve_kb(user_id, knowledge_base)
        if not kb:
            return f"Knowledge base '{knowledge_base}' not found."

        if command == "create":
            return await _create_note(user_id, kb, path, title, content, tags or [], date_str)
        elif command == "str_replace":
            return await _edit_note(user_id, kb, path, old_text, new_text)
        elif command == "append":
            return await _append_note(user_id, kb, path, content)

        return f"Unknown command: {command}"


MCP 工具实现确认了：`guide` 从 `knowledge_bases` 表取可用库；`search list` 从 `documents` 表列资料和 wiki；`read` 从 `document_pages` 读 PDF 页文本；`write` 更新 `documents.content`。我现在检查数据库连接和鉴权上下文，看看能否从本地脚本直接等价执行这些工具。


需要读取数据库访问层以等价调用 guide/search/read/write，是否允许？


Get-Content mcp\tools\helpers.py -TotalCount 320

需要读取 MCP helper 以解析知识库和路径，是否允许？


Get-Content mcp\config.py -TotalCount 220

需要读取配置定义以确认数据库连接变量名，是否允许？


需要读取 MCP 鉴权实现以确认如何确定当前用户，是否允许？