Stock Price MCP Integration

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

Add Stock Price Tool to AI Agent

Diagram

Quick Start

Install Dependencies

Create a conda environment and install the required packages:

bash

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"

Set API Key
Set your OpenAI API key as an environment variable in your terminal:
bash
```
export OPENAI_API_KEY=your_openai_api_key_here
```

Create the MCP Server

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

python

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')

Create the Agent

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

python

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?")

Run the Agent

Execute your script:

bash

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

Gradio UI Example

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

python

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:

bash

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