Building a Model Context Protocol (MCP) Server for Real-Time Financial Insights
This guide outlines the process of creating a Model Context Protocol (MCP) server that connects to Claude Desktop, enabling it to retrieve real-time stock news sentiment and identify daily top gainers and movers. This innovative solution addresses the limitation of most large language models (LLMs), which cannot directly access live financial data.
Key Functionalities
The MCP server will provide two primary functionalities:
- get_news_sentiment: Fetches the sentiment of stock-related news.
- get_top_movers: Identifies the top gainers and losers in the stock market.
Step-by-Step Implementation
1. Setting Up the Development Environment
Begin by setting up your programming environment. Use the following commands based on your operating system:
- Mac/Linux:
curl -LsSf - Windows (PowerShell):
powershell -ExecutionPolicy ByPass -c «irm
Create a new project directory and initialize it:
uv init stockNews
Activate the virtual environment:
- Mac/Linux:
source .venv/bin/activate - Windows:
.venv\Scripts\activate
Install the necessary dependencies:
uv add mcp httpx python-dotenv
2. Configuring Environment Variables
Create a .env file to store your API key for AlphaVantage. Follow these steps to obtain your free API key:
- Visit the AlphaVantage website.
- Click on the “Get free API key” button.
- Fill in your email and required information to receive your API key.
Add the following line to your .env file:
ALPHA_VANTAGE_API_KEY=your_api_key
3. Implementing the MCP Server
Create a new file named stockN and import the necessary packages:
from typing import Any
Set up the MCP server and define helper functions for querying AlphaVantage:
async def call_alpha_vantage(endpoint: str, params: dict[str, Any]) -> dict[str, Any] | None:
4. Tool Execution
Implement the tool execution handler for retrieving news sentiment and top movers:
@async def get_news_sentiment(ticker: str) -> str:
Handle the logic for fetching data and structuring the output for user inquiries.
5. Running and Testing the Server
Run the server and connect it with Claude Desktop:
if __name__ == "__main__":
Configure Claude to recognize the MCP server and test it by asking specific queries related to stock data.
Conclusion
This MCP-based server enhances Claude Desktop’s functionality by providing access to real-time financial data. By integrating the AlphaVantage API, users can obtain timely insights on stock sentiment and market movements, fostering improved decision-making in financial analysis. This development not only streamlines the process but also creates a more interactive and efficient user experience.
Final Thoughts
Embracing artificial intelligence technologies can significantly enhance business operations. By identifying automation opportunities and integrating AI tools effectively, organizations can improve efficiency and achieve measurable results. Start small, monitor effectiveness, and scale your AI initiatives for maximum impact.
