mcp_dev_basic
概览
An MCP (Model Context Protocol) server that gives an LLM host (Claude Desktop, an IDE, etc.) a tool for pulling documentation, instead of relying on the model's training data. Searches are scoped to a library's official docs site, fetched pages are chunked and embedded into a local vector store, and the tool returns only the most relevant excerpts — not raw scraped HTML — for the query. Embeddings run locally via Chroma's bundled model — no external embedding API key required. - Python 3.11+ - uv package manager - A Serper API key (free tier available) This starts the server over stdio — it's meant to be launched by an MCP host, not run standalone for interactive use (see below). To try the tool from a browser instead of an MCP host: Edit your Claude Desktop config (claude_desktop_config.json): Restart Claude Desktop, then ask something like "how do I use Chroma DB with LangChain?" — Claude will call get_docs and answer from the live docs instead of its training data.
README
以下为原文 README 节选,完整内容请查看原文。
Docs MCP Server
An MCP (Model Context Protocol) server that gives an LLM host (Claude Desktop, an IDE, etc.) a tool for pulling live documentation, instead of relying on the model’s training data. Searches are scoped to a library’s official docs site, fetched pages are chunked and embedded into a local vector store, and the tool returns only the most relevant excerpts — not raw scraped HTML — for the query.
How it works
LLM host (e.g. Claude Desktop)
│ calls get_docs(query, library) over MCP (stdio)
▼
main.py
│ 1. site-scoped Google search via Serper API
│ 2. fetches matching pages concurrently (httpx + asyncio.gather)
│ 3. strips HTML → plain text (BeautifulSoup)
▼
rag.py
│ 4. chunks each page (langchain-text-splitters)
│ 5. embeds + upserts chunks into a local Chroma collection,
│ scoped by library, keyed by URL (idempotent — re-fetching
│ a page doesn't duplicate it)
│ 6. queries the collection for the top-k chunks most relevant
│ to the original query
▼
Returns the top-k excerpts (with source URLs) back to the LLM host
Embeddings run locally via Chroma’s bundled model — no external embedding API key required.
Project structure
| File | Responsibility |
|---|---|
main.py |
MCP server entry point; defines the get_docs tool; search + fetch logic |
rag.py |
Chunking, embedding, and retrieval against a local Chroma vector store |
web.py |
Minimal FastAPI dashboard for exercising get_docs from a browser (no MCP host needed) |
tests/ |
pytest suite; all HTTP calls mocked via respx, vector store tests use an in-memory Chroma client |
.github/workflows/ci.yml |
Lint (ruff) + test on every push/PR |
Requirements
Setup
uv sync --all-groups
cp .env.example .env # then add your SERPER_API_KEY
Running the MCP server
uv run main.py
This starts the server over stdio — it’s meant to be launched by an MCP host, not run standalone for interactive use (see below).
Running the web dashboard
To try the tool from a browser instead of an MCP host:
uv run uvicorn web:app --reload
Then open http://127.0.0.1:8000.
Connecting to Claude Desktop
Edit your Claude Desktop config (claude_desktop_config.json):
{
"mcpServers": {
"docs": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/THIS/PROJECT",
"run",
"main.py"
]
}
}
}
Restart Claude Desktop, then ask something like “how do I use Chroma DB
with LangChain?” — Claude will call get_docs and answer from the live
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手?先看 mcpskill.org 教程 →配置
{
"mcpServers": {
"docs": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/THIS/PROJECT",
"run",
"main.py"
]
}
}
}