Provides tools to search and retrieve relevant documentation excerpts from LangChain's official docs, enabling up-to-date answers about LangChain usage.
Provides tools to search and retrieve relevant documentation excerpts from OpenAI's official docs, enabling up-to-date answers about OpenAI APIs and features.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Docs MCP Server Explain how to use LangChain with chat models, using live docs."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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 hostEmbeddings run locally via Chroma's bundled model — no external embedding API key required.
Related MCP server: Context7 MCP Clone
File | Responsibility |
MCP server entry point; defines the | |
Chunking, embedding, and retrieval against a local Chroma vector store | |
Minimal FastAPI dashboard for exercising | |
pytest suite; all HTTP calls mocked via | |
Lint ( |
uv sync --all-groups
cp .env.example .env # then add your SERPER_API_KEYuv run main.pyThis 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:
uv run uvicorn web:app --reloadThen open http://127.0.0.1:8000.
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
docs instead of its training data.
Currently langchain, llama-index, and openai — see DOCS_URLS in
main.py. Add your own by adding an entry mapping a library
name to its docs site.
uv run pytest -v
uv run ruff check .MIT — see LICENSE.
Search the latest docs for a given query and library, index the results
into a local RAG store, and return the most relevant excerpts.
Supports langchain, openai, and llama-index.
Args:
query: The query to search for (e.g. "Chroma DB")
library: The library to search in (e.g. "langchain")
Returns:
The most relevant text excerpts from the docs, with source URLs.
Inputs: query (required), library (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 1 tool
With only one tool, there is no possibility of confusing tools with overlapping purposes. The single tool's purpose is clearly defined as searching documentation and returning relevant excerpts.
The tool name 'get_docs' follows a clear verb_noun convention. Since there's only one tool, naming consistency is trivially maintained.
A single tool feels thin for a general-purpose docs server, especially since only three libraries are explicitly supported. Borderline for a narrow utility, but it could benefit from additional operations like listing supported libraries or retrieving raw document content.
For the stated purpose—searching documentation for a query and library—the tool fully covers the operation. There are no obvious missing features within the described scope.
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
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