A Model Context Protocol (MCP) server designed to empower Large Language Models (LLMs) with extensive financial analysis capabilities. It addresses the need for LLMs to access and interpret real-time market data, company fundamentals, technical indicators, and sentiment analysis, enabling sophisticated financial querying and decision support for developers building AI-driven financial tools.

How It Works

This project implements an MCP server that acts as a backend for financial data retrieval and analysis. It integrates with yfinance for market data and provides a suite of tools for accessing market movers, detailed ticker reports, options chains, historical prices, financial statements, ownership data, earnings calendars, and market sentiment indicators. The architecture optimizes data volume and supports optional extensions for advanced technical analysis via TA-Lib and earnings calendar functionality via Playwright.

Quick Start & Requirements

• Primary install command: uvx investor-agent using the uv package manager.
• Prerequisites: Python 3.12 or higher. uv installation script: https://astral.sh/uv/install.sh.
• Optional Dependencies: TA-Lib C Library for technical indicators (SMA, EMA, RSI, MACD, BBANDS). Playwright for earnings calendar functionality.
• Setup: Core installation is straightforward. Optional features require installing C libraries (TA-Lib) and browser dependencies (Playwright).

Highlighted Details

• Comprehensive financial data access: Supports market movers, detailed ticker analysis (news, recommendations, upgrades), options data filtering, historical prices, financial statements, ownership, insider trades, and earnings calendars.
• Market sentiment analysis: Integrates CNN Fear & Greed Index, Crypto Fear & Greed Index, and Google Trends for sentiment tracking.
• Technical analysis capabilities: Includes optional support for calculating common indicators like SMA, EMA, RSI, MACD, and BBANDS via TA-Lib.
• MCP Server architecture: Designed for integration with LLM frameworks like Claude via configuration, enabling programmatic financial data querying.

Maintenance & Community

The provided README does not detail specific contributors, sponsorships, or community channels (e.g., Discord/Slack).

Licensing & Compatibility

Licensed under the MIT License, permitting broad use and modification. Compatibility for commercial use is high due to the permissive MIT license.

Limitations & Caveats

Advanced features like technical analysis and the earnings calendar depend on optional external libraries (TA-Lib, Playwright) that require separate installation, potentially increasing setup complexity and platform-specific configuration.