adam-j-baron/mcp-finance-agents
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The Model Context Protocol (MCP) server provides financial data tools using a local Large Language Model (LLM) and a custom tool server.
Financial AI Agent with MCP Tools
This repository contains an experimental and educational project demonstrating how to build a financial AI agent using a local Large Language Model (LLM) and a custom tool server.
The project consists of two main files:
mcp_server_yfinance.py: A Model Context Protocol (MCP) server that provides financial data tools.mcp_client_yfinance.ipynb: A Jupyter Notebook that acts as a client to connect to the server, create a ReAct Agent, and perform financial analysis queries.
🚀 Getting Started
To run this project, you need to first start the MCP server, and then run the client notebook.
Step 1: Start the MCP Server
Open your terminal and run the Python server file. This will make the financial data tools available to your client.
python mcp_server_yfinance.py
Step 2: Run the Client Notebook
Open mcp_client_yfinance.ipynb in your Jupyter environment. You can then run the cells in the notebook sequentially to:
- Connect to the MCP server.
- Initialize the LLM (Ollama model).
- Create the ReAct agent.
- Ask questions and see the agent's financial analysis.
🔧 Project Details
This project uses the following key technologies:
- MCP (Model Context Protocol): For building and communicating with the tool server.
yfinance: The library used by the server to fetch financial data.ollama: A local LLM used to power the AI agent.langgraph: A library for building agents with reasoning and action capabilities (ReAct).
This is a simplified setup intended for learning. It's not designed for production use, as noted in the notebook, due to the way connections are handled for ease of experimentation.