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Learn_MCP Math Server

  • Apple
  • Linux

Learn_MCP Project Setup Guide

This guide will help you set up and run the Learn_MCP project, which demonstrates using a Model Context Protocol (MCP) math server with LangChain.

Prerequisites

  • Python 3.8 or higher (recommended: use a virtual environment)
  • uv (a fast Python package manager)
  • A valid GROQ API key (for ChatGroq)

1. Clone the Repository

git clone <your-repo-url> cd Learn_MCP
python -m venv .venv # On Windows: .venv\Scripts\activate # On macOS/Linux: source .venv/bin/activate

3. Install Dependencies with uv

uv pip install -r requirements.txt

Or, to use the lockfile (if present):

uv pip sync uv.lock

4. Set Up Environment Variables

Create a .env file in the project root with your GROQ API key:

GROQ_API_KEY=your_groq_api_key_here

5. Run the Math Server

The math server will be started automatically by the client script as a subprocess (mathserver.py). You do not need to start it manually.

6. Run the Client

uv pip run python client.py

Or simply:

python client.py

7. Troubleshooting

  • If you see ImportError: langchain_mcp_adapters.fastapi could not be resolved, ensure the package is installed or available in your environment.
  • If you get errors about missing modules, check your requirements.txt and install any missing dependencies.
  • Make sure your .env file is present and contains a valid GROQ_API_KEY.

8. Project Structure

  • client.py — Main client that connects to the math MCP server
  • mathserver.py — Math MCP server (started by the client)
  • requirements.txt — Python dependencies
  • .env — Environment variables (not committed to version control)

Feel free to update this README with additional details as your project evolves.

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security - not tested
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license - not found
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quality - not tested

A Model Context Protocol (MCP) server that demonstrates mathematical capabilities through a LangChain integration, allowing clients to perform math operations via the MCP protocol.

  1. Prerequisites
    1. Clone the Repository
      1. Create and Activate a Virtual Environment (optional but recommended)
        1. Install Dependencies with uv
          1. Set Up Environment Variables
            1. Run the Math Server
              1. Run the Client
                1. Troubleshooting
                  1. Project Structure

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