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.

5. 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?

6. 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.