MCP Server Project

This repository contains an implementation of a Model Context Protocol (MCP) server. This project demonstrates how to build and run a functional MCP server that can integrate with LLM clients like Claude Desktop.

Key Features

This MCP server provides a suite of tools focused on information retrieval and vector database management, primarily leveraging LangChain and ChromaDB:

• Targeted Documentation Search:

  • Quickly search the official documentation for popular AI/ML libraries:
    • Langchain
    • OpenAI
    • LlamaIndex
  • Retrieves relevant text snippets directly from the documentation sites.

• ChromaDB Vector Database Integration:

  • Setup: Create and initialize ChromaDB vector stores with your own text data and optional metadata.
  • Persistence: Option to persist databases to disk for later use or use in-memory stores.
  • Querying: Perform semantic searches on your ChromaDB instances to find relevant documents based on query similarity.
  • Demonstration: A built-in demo tool to showcase the setup and query capabilities with sample data.

• Powered by LangChain:

  • Utilizes LangChain for core functionalities like document handling, embedding management (using OpenAI Embeddings by default), and vector store interactions.

System Requirements

• Python 3.11 or higher (as specified in pyproject.toml)
• uv package manager
• Dependencies listed in pyproject.toml (e.g., mcp[cli], httpx, langchain)

Getting Started

1. Install uv Package Manager

If you don't have uv installed, you can install it using:

curl -LsSf https://astral.sh/uv/install.sh | sh

Restart your terminal after installation.

2. Project Setup

Clone this repository (if you haven't already) and navigate into the project directory:

# cd /path/to/your/mcp-server

Create a virtual environment and install dependencies:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -r requirements.txt # Or use uv pip install -e . if setup.py or pyproject.toml is configured for editable install
# Based on your pyproject.toml, you might also directly use:
# uv add beautifulsoup4 httpx "mcp[cli]" langchain langchain-community langchain-core chromadb
# Or more simply if pyproject.toml is complete:
# uv sync

(Note: Ensure your pyproject.toml is complete or you have a requirements.txt for uv pip install -r requirements.txt. uv sync is often preferred if pyproject.toml defines all dependencies.)

3. Running the Server

To start the MCP server, run:

uv run main.py

The server will start and be ready to accept connections.

Connecting to Claude Desktop

To connect this MCP server to Claude Desktop:

1. Ensure Claude Desktop is installed.

2. Edit the Claude Desktop configuration file located at ~/Library/Application Support/Claude/claude_desktop_config.json (on macOS).

3. Add or update the mcpServers section:

   {
       "mcpServers": {
           "mcp-server": { // You can choose any name
               "command": "/full/path/to/your/.venv/bin/uv", // Use absolute path to uv in your venv
               "args": [
                   "run",
                   "main.py"
               ],
               "dir": "/full/path/to/your/mcp-server" // Absolute path to this project directory
           }
       }
   }
   

   Important: Replace /full/path/to/your/... with the correct absolute paths on your system. Using the uv from your project's virtual environment is recommended.

4. Restart Claude Desktop.

Acknowledgements

This project is largely based on the mcp-server-example generously provided by Alejandro AO. We have adapted and utilized significant portions of his original work to build this server. We extend our sincere gratitude to Alejandro for his excellent example and for making his code available to the community.

You can find Alejandro AO's original repository here: https://github.com/alejandro-ao/mcp-server-example.

License

This project is licensed under the MIT License. See the LICENSE file for more details (if one exists).