Which integrations are available for this server?

Provides access to CNN's Fear & Greed Index for market sentiment analysis, including current values and historical data across multiple indicators like put/call options, market volatility, and safe haven demand. Enables tracking of relative search interest through Google Trends for market-related keywords, providing sentiment indicators based on search volumes over configurable time periods.

How do I use Agentic-Investor?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Agentic-Investor show me the current market movers and top gainers" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Agentic-Investor

by jon-fox

Overview Schema Source Related Servers Discussions Admin

Python

Remote

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.

Related MCP server: StockMCP

Architecture & Performance

Robust Caching & Error Handling Strategy:

yfinance[nospam] → Built-in smart caching + rate limiting for Yahoo Finance API
hishel → HTTP response caching for external APIs (CNN, crypto, earnings data)
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

# Core features only
uvx agentic-investor

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

# With Alpaca intraday data (requires Alpaca API keys)
uvx "agentic-investor[alpaca]"

# With all optional features
uvx "agentic-investor[ta,alpaca]"

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

npm i mcp-remote

Start the server and add to your claude_desktop_config.json:

uv run python -m agentic_investor.server

{
  "mcpServers": {
    "Agentic-Investor": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "http://0.0.0.0:8000/mcp",
        "--allow-http"
      ]
    }
  }
}

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

MCP Inspector

npx @modelcontextprotocol/inspector uvx agentic-investor

Debug Logging

Enable detailed debug logging for development and troubleshooting:

# Enable debug logging
export DEBUG_LOGGING=true

# Run with debug logging
DEBUG_LOGGING=true python -m agentic_investor.server

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

License

MIT License. See LICENSE file for details.

Available Tools

14 tools

calculate_technical_indicatorA

Calculate professional technical analysis indicators using TA-Lib including: Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD with signal line and histogram), and Bollinger Bands (upper/middle/lower bands with standard deviation). Use this when asked about technical analysis, chart indicators, overbought/oversold conditions (RSI), trend identification (moving averages), momentum signals (MACD), volatility analysis (Bollinger Bands), support/resistance levels, crossover signals, or trading indicators. Supports customizable time periods (5-200 periods), lookback windows (1 month to 5 years), and moving average types. Returns up to 1000 data points. Example: "Calculate RSI for AAPL" or "Show me 50-day and 200-day moving averages for SPY".