unimcp 0.1.0

A simple client library to connect to any MCP server and interact with LLMs.

UniMCP

A simple client library to connect to any MCP server and interact with tools seamlessly via code or through an LLM.

Installation

pip install unimcp

(Note: While in development, you can install it locally using pip install -e .)

Features

• Simple MCP Client: Connect to any MCP server, list available tools, and call them directly with just a few lines of Python.
• LLM Integration: Built-in wrapper to hook your MCP server tools directly into OpenAI (or any OpenAI-compatible API), enabling an LLM agent out-of-the-box.

Usage

1. Using only the MCP Client (No LLM)

import asyncio
from unimcp import UniClient

async def main():
    # Connect to your MCP server
    async with UniClient("http://localhost:8000/sse") as client:
        # 1. Get available tools
        tools = await client.get_tools()
        print("Available tools:", [t.name for t in tools])

        # 2. Call a specific tool manually
        result = await client.call_tool("my_tool_name", {"arg1": "value"})
        print("Tool Result:", result)

if __name__ == "__main__":
    asyncio.run(main())

2. Using the LLM Client

This utilizes OpenAI's python library under the hood. You can configure it using environment variables (OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL) or pass them directly.

import asyncio
from unimcp import UniClient, UniLLM

async def main():
    async with UniClient("http://localhost:8000/sse") as client:
        
        # Initialize the LLM with the MCP client
        # It automatically detects OPENAI_API_KEY from environment variables
        llm = UniLLM(client, model_name="gpt-4o")
        
        # Optionally set a system prompt
        llm.set_system_prompt("You are a helpful assistant with access to MCP tools.")

        # Chat with the LLM (it will automatically use the tools if needed)
        response = await llm.chat("Can you perform an action using your tools?")
        print("AI:", response)

if __name__ == "__main__":
    asyncio.run(main())

Environment Variables

The UniLLM automatically respects the following standard environment variables:

• OPENAI_API_KEY: Your API key.
• OPENAI_BASE_URL: For connecting to local LLMs (like LMStudio, Ollama) or other providers.
• OPENAI_MODEL: Default model to use (fallback is gpt-4o).