investor-agent 1.3.2

A Model Context Protocol server for an investor agent

investor-agent: A Financial Analysis MCP Server

Overview

The investor-agent is a Model Context Protocol (MCP) server that provides comprehensive financial insights and analysis to Large Language Models. It leverages real-time market data, fundamental and technical analysis to deliver:

• Market Movers: Top gainers, losers, and most active stocks with support for different market sessions
• Ticker Analysis: Company overview, news, metrics, analyst recommendations, and upgrades/downgrades
• Options Data: Filtered options chains with customizable parameters
• Historical Data: Price trends and earnings history
• Financial Statements: Income, balance sheet, and cash flow statements
• Ownership Analysis: Institutional holders and insider trading activity
• Earnings Calendar: Upcoming earnings announcements with date filtering
• Market Sentiment: CNN Fear & Greed Index, Crypto Fear & Greed Index, and Google Trends sentiment analysis
• Technical Analysis: SMA, EMA, RSI, MACD, BBANDS indicators (optional)

The server integrates with yfinance for market data and automatically optimizes data volume for better performance.

Architecture & Performance

Robust Caching & Error Handling Strategy:

1. yfinance[nospam] → Built-in smart caching + rate limiting for Yahoo Finance API
2. hishel → HTTP response caching for external APIs (CNN, crypto, earnings data)
3. tenacity → Retry logic with exponential backoff for transient failures

This multi-layered approach ensures reliable data delivery while respecting API rate limits and minimizing redundant requests.

Prerequisites

• Python: 3.12 or higher
• Package Manager: uv. Install if needed:

  curl -LsSf https://astral.sh/uv/install.sh | sh
  

Optional Dependencies

• TA-Lib C Library: Required for technical indicators. Follow official installation instructions.

Installation

Quick Start

# Core features only
uvx investor-agent

# With technical indicators (requires TA-Lib)
uvx "investor-agent[ta]"

Tools

Market Data

• get_market_movers(category="most-active", count=25, market_session="regular") - Market movers data including top gainers, losers, or most active stocks. Supports different market sessions (regular/pre-market/after-hours) for most-active category. Returns up to 100 stocks with cleaned percentage changes, volume, and market cap data
• get_ticker_data(ticker, max_news=5, max_recommendations=5, max_upgrades=5) - Comprehensive ticker report with essential field filtering and configurable limits for news, analyst recommendations, and upgrades/downgrades
• get_options(ticker_symbol, num_options=10, start_date=None, end_date=None, strike_lower=None, strike_upper=None, option_type=None) - Options data with advanced filtering by date range (YYYY-MM-DD), strike price bounds, and option type (C=calls, P=puts)
• get_price_history(ticker, period="1mo") - Historical OHLCV data with intelligent interval selection: daily intervals for periods ≤1y, monthly intervals for periods ≥2y to optimize data volume
• get_financial_statements(ticker, statement_types=["income"], frequency="quarterly", max_periods=8) - Financial statements with parallel fetching support. Returns dict with statement type as key
• get_institutional_holders(ticker, top_n=20) - Major institutional and mutual fund holders data
• get_earnings_history(ticker, max_entries=8) - Historical earnings data with configurable entry limits
• get_insider_trades(ticker, max_trades=20) - Recent insider trading activity with configurable trade limits
• get_nasdaq_earnings_calendar(date=None, limit=100) - Upcoming earnings announcements using Nasdaq API (YYYY-MM-DD format, defaults to today).

Market Sentiment

• get_cnn_fear_greed_index(indicators=None) - CNN Fear & Greed Index with selective indicator filtering. Available indicators: fear_and_greed, fear_and_greed_historical, put_call_options, market_volatility_vix, market_volatility_vix_50, junk_bond_demand, safe_haven_demand
• get_crypto_fear_greed_index() - Current Crypto Fear & Greed Index with value, classification, and timestamp
• get_google_trends(keywords, period_days=7) - Google Trends relative search interest for market-related keywords. Requires a list of keywords to track (e.g., ["stock market crash", "bull market", "recession", "inflation"]). Returns relative search interest scores that can be used as sentiment indicators.

Technical Analysis

• calculate_technical_indicator(ticker, indicator, period="1y", timeperiod=14, fastperiod=12, slowperiod=26, signalperiod=9, nbdev=2, matype=0, num_results=100) - Calculate technical indicators (SMA, EMA, RSI, MACD, BBANDS) with configurable parameters and result limiting. Returns dictionary with price_data and indicator_data as CSV strings. matype values: 0=SMA, 1=EMA, 2=WMA, 3=DEMA, 4=TEMA, 5=TRIMA, 6=KAMA, 7=MAMA, 8=T3. Requires TA-Lib library.

Usage with MCP Clients

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "investor": {
      "command": "uvx",
      "args": ["investor-agent"]
    }
  }
}

Local Testing

For local development and testing, use the included chat.py script:

# Install dev dependencies
uv sync --group dev

# Set up your API key
export OPENAI_API_KEY="your-api-key"  # or ANTHROPIC_API_KEY, GEMINI_API_KEY, etc.

# Optional: Set custom model (defaults to openai:gpt-5-mini)
export MODEL_IDENTIFIER="your-preferred-model"

# Run the chat interface
python chat.py

For available model providers and identifiers, see the pydantic-ai documentation.

Debugging

npx @modelcontextprotocol/inspector uvx investor-agent

License

MIT License. See LICENSE file for details.