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MCP REST API and CLI Client

A simple REST API and CLI client to interact with Model Context Protocol (MCP) servers.

Key Features

1. MCP-Compatible Servers

  • Supports any MCP-compatible servers servers.
  • Pre-configured default servers:
    • SQLite (test.db has been provided with sample products data)
    • Brave Search
  • Additional MCP servers can be added in the mcp-server-config.json file

2. Integrated with LangChain

  • Leverages LangChain to execute LLM prompts.
  • Enables multiple MCP servers to collaborate and respond to a specific query simultaneously.

3. LLM Provider Support

  • Compatible with any LLM provider that supports APIs with function capabilities.
  • Examples:
    • OpenAI
    • Claude
    • Gemini
    • AWS Nova
    • Groq
    • Ollama
    • Essentially all LLM providers are supported as long as they provide a function-based API. Please refer langchain documentation for more details.

Setup

  1. Clone the repository:

    git clone https://github.com/rakesh-eltropy/mcp-client.git

  2. Navigate to the Project Directory After cloning the repository, move to the project directory:

    cd mcp-client

  3. Set the OPENAI_API_KEY environment variable:

    export OPENAI_API_KEY=your-openai-api-key

    You can also set the OPENAI_API_KEY in the mcp-server-config.json file.

    You can also set the provider and model in the mcp-server-config.json file. e.g. provider can be ollama and model can be llama3.2:3b.

4.Set the BRAVE_API_KEY environment variable:

export BRAVE_API_KEY=your-brave-api-key

You can also set the BRAVE_API_KEY in the mcp-server-config.json file. You can get the free BRAVE_API_KEY from Brave Search API.

  1. Running from the CLI:

    uv run cli.py

    To explore the available commands, use the help option. You can chat with LLM using chat command. Sample prompts:

      What is the capital city of India?
      Search the most expensive product from database and find more details about it from amazon?

  2. Running from the REST API:

    uvicorn app:app --reload

    You can use the following curl command to chat with llm:

    curl -X POST -H "Content-Type: application/json" -d '{"message": "list all the products from my local database?"}' http://localhost:8000/chat

    You can use the following curl command to chat with llm with streaming:

    curl -X POST -H "Content-Type: application/json" -d '{"message": "list all the products from my local database?", "streaming": true}' http://localhost:8000/chat

Contributing

Feel free to submit issues and pull requests for improvements or bug fixes.