Stock Price MCP Integration

Guide for integrating stock price retrieval capabilities with PraisonAI agents using MCP

Add Stock Price Tool to AI Agent

Quick Start

1. Install Dependencies

   Create a conda environment and install the required packages:

   zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda create -n windsurf python=3.10 -y"
   zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && pip install praisonaiagents mcp yfinance"
   
2. Set API Key

   Set your OpenAI API key as an environment variable in your terminal:

   export OPENAI_API_KEY=your_openai_api_key_here
   
3. Create the MCP Server

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

   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')
   
4. Create the Agent

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

   from praisonaiagents import Agent, MCP
   import os
   
   # Get the path to your Python interpreter and the server file
   python_path = os.getenv("PYTHON_PATH", "/path/to/your/python")
   server_path = os.getenv("SERVER_PATH", "/path/to/your/stock_price_server.py")
   
   # Create the agent with the stock price MCP tool
   agent = Agent(
       instructions="""You are a helpful assistant that can check stock prices.
       Use the available tools when relevant to answer user questions.""",
       llm="gpt-4o-mini",
       tools=MCP(f"{python_path} {server_path}")
   )
   
   agent.start("What is the stock price of Tesla?")
   
5. Run the Agent

   Execute your script:

   zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && python stock_price_agent.py"
   

Requirements

• Python 3.10 or higher
• yfinance package
• mcp-python-sdk package
• praisonaiagents package
• OpenAI API key (for the agent’s LLM)

Gradio UI Example

You can also create a simple web UI for your stock price agent using Gradio:

from praisonaiagents import Agent, MCP
import gradio as gr
import os

# Get the path to your Python interpreter and the server file
python_path = os.getenv("PYTHON_PATH", "/path/to/your/python")
server_path = os.getenv("SERVER_PATH", "/path/to/your/stock_price_server.py")

# Create the agent with the stock price MCP tool
agent = Agent(
    instructions="""You are a helpful assistant that can check stock prices.
    Use the available tools when relevant to answer user questions.""",
    llm="gpt-4o-mini",
    tools=MCP(f"{python_path} {server_path}")
)

def chat(message, history):
    return agent.chat(message)

demo = gr.ChatInterface(
    chat,
    title="Stock Price Assistant",
    description="Ask about any stock price and get real-time information",
    theme="soft"
)

if __name__ == "__main__":
    demo.launch()

Install Gradio with:

zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && pip install gradio"