mcp-finance-search-agents

adam-j-baron/mcp-finance-search-agents

3.1

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This project demonstrates how to build a financial AI agent using a local LLM and a custom tool server with live web search capabilities.

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Financial AI Agent with MCP Tools, including Live Web Search

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 tool server includes live web search capability.

The project consists of two main files:

  • mcp_server_yfinance_search.py: A Model Context Protocol (MCP) server that provides financial data and live web search tools.
  • mcp_client_yfinance_search.ipynb: A Jupyter Notebook that acts as a client to connect to the server, creates a ReAct Agent, and performs a sample financial analysis query.

🚀 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 and live web search tools available to your client.

python mcp_server_yfinance_search.py

Ensure that OLLAMA_API_KEY environment variable is set from the Terminal beforehand, or the MCP Server will not run. Get your free Ollama API Key here: https://ollama.com/settings/keys

For Windows PowerShell users, the command is:

$Env:OLLAMA_API_KEY = "your_api_key"

Step 2: Run the Client Notebook

Open mcp_client_yfinance_search.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. Ollama recently added web_search and web_fetch tools for access to live web results.
  • 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.