Agentic-Investor: A Financial Analysis MCP Server

Overview

The Agentic-Investor 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)

• Intraday Data: 15-minute historical stock bars via Alpaca API (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.

• Alpaca API: Required for intraday stock data. Get free API keys at Alpaca Markets.

Installation

Quick Start

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).

• fetch_intraday_data(stock, window=200) - Fetch 15-minute historical stock bars using Alpaca API. Returns CSV string with timestamp and close price data in EST timezone. Requires agentic-investor[alpaca] installation and ALPACA_API_KEY/ALPACA_API_SECRET environment variables.

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 locally

Install mcp-remote

https://www.npmjs.com/package/mcp-remote

Start the server and add to your claude_desktop_config.json:

Local Testing

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

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

Debugging

MCP Inspector

Debug Logging

Enable detailed debug logging for development and troubleshooting:

See DEBUG_LOGGING.md for more details on what gets logged and how to use it.

License

MIT License. See LICENSE file for details.