> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Stock Price MCP Integration

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

## Add Stock Price Tool to AI Agent

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    In[Query] --> Agent[AI Agent]
    Agent --> Tool[Stock Price MCP]
    Tool --> Agent
    Agent --> Out[Answer]
    
    style In fill:#8B0000,color:#fff
    style Agent fill:#2E8B57,color:#fff
    style Tool fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff
```

## Quick Start

<Steps>
  <Step title="Install Dependencies">
    Create a conda environment and install the required packages:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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"
    ```
  </Step>

  <Step title="Set API Key">
    Set your OpenAI API key as an environment variable in your terminal:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    export OPENAI_API_KEY=your_openai_api_key_here
    ```
  </Step>

  <Step title="Create the MCP Server">
    Create a new file `stock_price_server.py` with the following code:

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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')
    ```
  </Step>

  <Step title="Create the Agent">
    Create a new file `stock_price_agent.py` with the following code:

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    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?")
    ```
  </Step>

  <Step title="Run the Agent">
    Execute your script:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && python stock_price_agent.py"
    ```
  </Step>
</Steps>

<Note>
  **Requirements**

  * Python 3.10 or higher
  * yfinance package
  * mcp-python-sdk package
  * praisonaiagents package
  * OpenAI API key (for the agent's LLM)
</Note>

## Gradio UI Example

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

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && pip install gradio"
```