MCP-123

🚀 Features

• Ultra-minimal setup: Start a server or client in 2 lines.

• Easy tool creation: Write normal functions in your tools.py file—no decorators or special wrappers needed—and they get included as tools that your MCP server can use automatically.

• OpenAI integration: The client uses your OpenAI API key to answer questions, calling tools as needed.

Related MCP server: MCP Python Tutorial

🖥️ Quickstart

1. Install Requirements

pip install -r requirements.txt

2. Create Your Tools

Define your functions in tools.py. No decorators needed, they are automatically added to your MCP server as tools. For example:

def add(a: int, b: int) -> int:

    """Add two numbers."""

    return a + b

3. Start the MCP Server (2 lines)

from mcp123 import server

server.run_server("tools.py", port=9999)

4. Set up the MCP Client (2 lines)

from mcp123.client import McpClient

client = McpClient("http://localhost:9999", "sk-...your OpenAI key...")

5. Use the MCP Client

answer = client.ask("Add 15 and 14.")

print("Answer:", answer)

6. Close the MCP Client when you are done

client.close()

🚀 Features

• Ultra-minimal setup: Start a server or client in 2 lines.

• Easy tool creation: Write normal functions in your tools.py file—no decorators or special wrappers needed—and they get included as tools that your MCP server can use automatically.

• OpenAI integration: The client uses your OpenAI API key to answer questions, calling tools as needed.

📝 How It Works

• Server: Loads all top-level functions from tools.py and exposes them as MCP tools via HTTP.

• Client: Discovers available tools, sends prompts to OpenAI, and automatically calls tools if needed.

🛠️ Example Output

When you run the client, you’ll see:

Tools discovered:

 [ ...list of tools... ]



Answer: 29

🔑 Requirements

• Python 3.11+

• OpenAI API key (for the client)

📢 Why MCP123?

• Zero boilerplate: No need to write schemas or wrappers—just write functions.

• LLM-native: Designed for seamless LLM tool use.

• Extensible: Add more tools by simply adding functions.

🤝 Credits

• Built with FastMCP

• Inspired by the Model Context Protocol (MCP)

📬 Feedback & Contributions

Pull requests and issues are welcome, but only if they are in ALL-CAPS.