Documentation MCP Server
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., "@Documentation MCP Server search LangChain documentation for vector stores"
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.
Documentation MCP Server ππ
A Model Context Protocol (MCP) server that enables Claude to search and access documentation from popular libraries like LangChain, LlamaIndex, and OpenAI directly within conversations.
What is MCP? π€
MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to Large Language Models. Think of it as a universal connector that lets AI assistants like Claude access external data sources and tools.


Related MCP server: Needle MCP Server
Features β¨
Documentation Search Tool: Search through documentation of popular AI libraries
Supported Libraries:
LangChain π
LlamaIndex π¦
OpenAI π€
Smart Extraction: Intelligently parses HTML content to extract the most relevant information
Configurable Results: Limit the amount of text returned based on your needs
How It Works π οΈ
The server uses the Serper API to perform Google searches with site-specific queries
It fetches the content from the search results
BeautifulSoup extracts the most relevant text from main content areas
Claude can access this information through the
get_docstool
System Requirements π₯οΈ
Python 3.11 or higher
uvpackage managerA Serper API key
Setup Instructions π
1. Install uv Package Manager
curl -LsSf https://astral.sh/uv/install.sh | sh2. Clone and Set Up the Project
# Clone or download the project
cd documentation
# Create and activate virtual environment
uv venv
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate
# Install dependencies
uv pip install -e .3. Configure the Serper API Key
Create a .env file in the project directory with your Serper API key:
SERPER_API_KEY=your_serper_api_key_hereYou can get a Serper API key by signing up at serper.dev.
4. Configure Claude Desktop
Edit your Claude Desktop configuration file at:
Windows:
/C:/Users/[Your Username]/AppData/Roaming/Claude/claude_desktop_config.jsonmacOS:
~/Library/Application Support/Claude/claude_desktop_config.json
Add the following to the mcpServers section:
"documentation": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/YOUR/documentation",
"run",
"main.py"
]
}Replace /ABSOLUTE/PATH/TO/YOUR/documentation with the absolute path to your project directory.
5. Restart Claude Desktop
Close and reopen Claude Desktop to apply the new configuration.
Using the Documentation Tool π§©
Once connected, you can ask Claude to use the documentation tool:
"Can you look up information about vector stores in LangChain documentation?"
Claude will use the get_docs tool to search for relevant information and provide you with documentation excerpts.
Tool Parameters π
The get_docs tool accepts the following parameters:
query: The search term (e.g., "vector stores", "embedding models")library: Which library to search (langchain, llama-index, or openai)max_chars: Maximum characters to return (default: 1000)
Troubleshooting π οΈ
Claude can't find the server: Verify the path in
/C:/Users/fcbsa/AppData/Roaming/Claude/claude_desktop_config.jsonis correctSearch returns no results: Check your Serper API key and internet connection
Timeout errors: The server might be experiencing connectivity issues or rate limits
License π
This project is provided as an educational example of MCP server implementation.
Acknowledgements π
Built using the MCP SDK
Powered by Serper API for Google search integration
Uses BeautifulSoup4 for HTML parsing
Inspired by the growing MCP community
This MCP server enhances Claude's capabilities by providing direct access to documentation resources. Explore, learn, and build better AI applications with contextual knowledge from the docs!
Available Tools
1 toolget_docsA
Search the docs for a given query and library.
Supports langchain, llama-index, and openai.
Args:
query: The query to search for (e.g.: "Chroma DB").
library: The library to search in. One of langchain, llama-index, openai.
max_chars: Maximum characters to return (default: 1000 for free tier).
Returns:
Text from the documentation.
Inputs: query (required), library (required), max_chars.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogβ¦
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'get_docs' has a clearly defined and distinct purpose of searching documentation for specified libraries.
With only one tool, naming consistency is inherently perfect. The tool name 'get_docs' follows a clear verb_noun pattern that would be consistent if more tools were added.
A single tool for a documentation server feels insufficient for the apparent scope. While the tool covers search functionality, typical documentation servers would benefit from additional tools like list_libraries, get_document, or browse_topics to provide more complete coverage.
The tool surface is severely incomplete for a documentation server. While search functionality is provided, there are significant gaps: no way to list available libraries, browse documentation structure, get specific documents by reference, or access metadata about documentation resources. This will likely cause agent failures when trying to comprehensively work with documentation.
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