Custom Python MCP Server

Guide for creating and using custom Python MCP servers with PraisonAI agents

Custom Python MCP Server

Quick Start

1. Create MCP Server

   Create a new file app.py with your custom MCP server implementation:

   import yfinance as yf
   from mcp.server.fastmcp import FastMCP
   
   mcp = FastMCP("stock_prices")
   
   @mcp.tool()
   async def get_stock_price(ticker: str) -> str:
       """Get the current stock price for a given ticker symbol.
       
       Args:
           ticker: Stock ticker symbol (e.g., AAPL, MSFT, GOOG)
           
       Returns:
           Current stock price as a string
       """
       if not ticker:
           return "No ticker provided"
       try:
           stock = yf.Ticker(ticker)
           info = stock.info
           current_price = info.get('currentPrice') or info.get('regularMarketPrice')
           if not current_price:
               return f"Could not retrieve price for {ticker}"
           return f"${current_price:.2f}"
           
       except Exception as e:
           return f"Error: {str(e)}"
   
   if __name__ == "__main__":
       mcp.run(transport='stdio')
   
2. Install Dependencies

   Install the required dependencies in a conda environment:

   pip install yfinance mcp
   
3. Create Agent Integration

   Create a new file stock_agent.py with the following code:

   from praisonaiagents import Agent, MCP
   
   agent = Agent(
       instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
       Use the available tools when relevant to answer user questions.""",
       llm="gpt-4o-mini",
       tools = MCP("/path/to/python /path/to/app.py")
   )
   
   # NOTE: Replace with your actual Python path and app.py file path
   
   agent.start("What is the stock price of Tesla?")
   
4. Run the Agent

   Execute your script:

   python stock_agent.py
   

Requirements

• Python 3.10 or higher
• Conda for environment management
• yfinance package for stock data
• mcp-python-sdk for MCP server implementation
• OpenAI API key (for the agent’s LLM)

Features

Custom Tools

Create your own custom tools with Python.

MCP Integration

Seamless integration with Model Context Protocol.

Type Hints

Strong typing for better code reliability.

Async Support

Built-in support for asynchronous functions.

Implementation Details

FastMCP Class

The FastMCP class from the mcp-python-sdk package provides a simple way to create MCP servers in Python:

from mcp.server.fastmcp import FastMCP

# Create an MCP server with a name
mcp = FastMCP("my_tools")

# Define a tool using the @mcp.tool decorator
@mcp.tool()
async def my_tool(param1: str, param2: int) -> str:
    """Tool description with clear documentation.
    
    Args:
        param1: Description of param1
        param2: Description of param2
        
    Returns:
        Description of the return value
    """
    # Tool implementation
    return f"Processed {param1} with {param2}"

# Run the server with stdio transport
if __name__ == "__main__":
    mcp.run(transport='stdio')

Agent Integration

To use your custom MCP server with PraisonAI agents, use the MCP class to specify the command to run your Python script:

from praisonaiagents import Agent, MCP

agent = Agent(
    instructions="Agent instructions",
    llm="gpt-4o-mini",
    tools=MCP(
        command="python",  # Or full path to Python
        args=["path/to/your/mcp_server.py"]  # Path to your MCP server script
    )
)