MaverickMCP - Personal Stock Analysis MCP Server

MaverickMCP is a personal-use FastMCP 2.0 server that provides professional-grade financial data analysis, technical indicators, and portfolio optimization tools directly to your Claude Desktop interface. Built for individual traders and investors, it offers comprehensive stock analysis capabilities without any authentication or billing complexity.

The server comes pre-seeded with all 520 S&P 500 stocks and provides advanced screening recommendations across multiple strategies. It runs locally with HTTP/SSE/STDIO transport options for seamless integration with Claude Desktop and other MCP clients.

📖 Read the full build guide: How to Build an MCP Stock Analysis Server

🌟 Why MaverickMCP?

MaverickMCP provides professional-grade financial analysis tools directly within your Claude Desktop interface. Perfect for individual traders and investors who want comprehensive stock analysis capabilities without the complexity of expensive platforms or commercial services.

🚀 Key Benefits:

No Setup Complexity: Simple make dev command gets you running (or uv sync + make dev)
Modern Python Tooling: Built with uv for lightning-fast dependency management
Claude Desktop Integration: Native MCP support for seamless AI-powered analysis
Comprehensive Analysis: 29+ financial tools covering technical indicators, screening, and portfolio optimization
Smart Caching: Redis-powered performance with graceful fallbacks
Fast Development: Hot reload, smart error handling, and parallel processing
Open Source: MIT licensed, community-driven development
Educational Focus: Perfect for learning financial analysis and MCP development

Features

🚀 Pre-seeded Database: 520 S&P 500 stocks with comprehensive screening recommendations
🚀 Fast Development: Comprehensive Makefile, smart error handling, hot reload, and parallel processing
Stock Data Access: Historical and real-time stock data with intelligent caching
Technical Analysis: 20+ indicators including SMA, EMA, RSI, MACD, Bollinger Bands, and more
Stock Screening: Multiple strategies (Maverick Bullish/Bearish, Trending Breakouts) with parallel processing
Portfolio Tools: Correlation analysis, returns calculation, and optimization
Market Data: Sector performance, market movers, and earnings information
Smart Caching: Redis-powered performance with automatic fallback to in-memory storage
Database Support: SQLAlchemy integration with PostgreSQL/SQLite (defaults to SQLite)
Multi-Transport Support: HTTP, SSE, and STDIO transports for all MCP clients

🚀 Quick Start

Prerequisites

Python 3.12+: Core runtime environment
uv: Modern Python package manager (recommended)
Redis (optional, for enhanced caching)
PostgreSQL or SQLite (optional, for data persistence)

Installing uv (Recommended)

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

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# Alternative: via pip
pip install uv

Installation

Option 1: Using uv (Recommended - Fastest)

# Clone the repository
git clone https://github.com/wshobson/maverick-mcp.git
cd maverick-mcp

# Install dependencies and create virtual environment in one command
uv sync

# Copy environment template
cp .env.example .env
# Add your Tiingo API key (free at tiingo.com)

Option 2: Using pip (Traditional)

# Clone the repository
git clone https://github.com/wshobson/maverick-mcp.git
cd maverick-mcp

# Create virtual environment and install
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

# Copy environment template
cp .env.example .env
# Add your Tiingo API key (free at tiingo.com)

Start the Server

# One command to start everything (includes S&P 500 data seeding on first run)
make dev

# The server is now running with:
# - HTTP endpoint: http://localhost:8000/mcp
# - SSE endpoint: http://localhost:8000/sse
# - 520 S&P 500 stocks pre-loaded with screening data

Connect to Claude Desktop

Claude Desktop uses STDIO to communicate with mcp-remote, which then connects to your HTTP server:

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:8000/mcp"]
    }
  }
}

Alternate option: Direct STDIO Connection (Development Only)

Claude Desktop directly connects via STDIO without any HTTP layer:

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "uv",
      "args": [
        "run",
        "python",
        "-m",
        "maverick_mcp.api.server",
        "--transport",
        "stdio"
      ],
      "cwd": "/path/to/maverick-mcp"
    }
  }
}

Note: The mcp-remote package bridges Claude Desktop's STDIO-only support to HTTP/SSE servers. For native remote server support, use Claude.ai web interface instead of Claude Desktop.

That's it! MaverickMCP tools will now be available in your Claude Desktop interface.

Connect to Other MCP Clients

⚠️ Transport Compatibility Warning: Different MCP clients support different transport methods. Using the wrong configuration will result in connection failures. Please use the exact configuration for your client.

Transport Compatibility Matrix

MCP Client	STDIO	HTTP	SSE	Notes
Claude Desktop	✅	❌	❌	STDIO-only, requires mcp-remote for HTTP/SSE
Cursor IDE	✅	❌	✅	Supports STDIO and SSE
Claude Code	✅	✅	✅	Supports all transports
Continue.dev	✅	❌	✅	Supports STDIO and SSE
Windsurf IDE	✅	❌	✅	Supports STDIO and SSE
Goose CLI	✅	❌	✅	Supports STDIO and SSE

Claude Desktop (Most Popular) - STDIO Only

⚠️ Important: Claude Desktop ONLY supports STDIO transport. It cannot directly connect to HTTP or SSE servers and requires the mcp-remote bridge tool.

For HTTP Server Connection (Recommended):

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:8000/mcp"]
    }
  }
}

For Direct STDIO (Development Only):

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "uv",
      "args": [
        "run",
        "python",
        "-m",
        "maverick_mcp.api.server",
        "--transport",
        "stdio"
      ]
    }
  }
}

Config Location:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Cursor IDE - STDIO and SSE

Option 1: STDIO (via mcp-remote):

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:8000/mcp"]
    }
  }
}

Option 2: Direct SSE:

{
  "mcpServers": {
    "maverick-mcp": {
      "url": "http://localhost:8000/sse"
    }
  }
}

Config Location: Cursor → Settings → MCP Servers

Claude Code CLI - All Transports

HTTP Transport (Recommended):

claude mcp add --transport http maverick-mcp http://localhost:8000/mcp

SSE Transport (Legacy):

claude mcp add --transport sse maverick-mcp http://localhost:8000/sse

STDIO Transport (Development):

claude mcp add maverick-mcp uv run python -m maverick_mcp.api.server --transport stdio

Continue.dev - STDIO and SSE

Option 1: STDIO (via mcp-remote):

{
  "experimental": {
    "modelContextProtocolServer": {
      "transport": {
        "type": "stdio",
        "command": "npx",
        "args": ["-y", "mcp-remote", "http://localhost:8000/mcp"]
      }
    }
  }
}

Option 2: Direct SSE:

{
  "mcpServers": {
    "maverick-mcp": {
      "url": "http://localhost:8000/sse"
    }
  }
}

Config Location: ~/.continue/config.json

Windsurf IDE - STDIO and SSE

Option 1: STDIO (via mcp-remote):

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:8000/mcp"]
    }
  }
}

Option 2: Direct SSE:

{
  "mcpServers": {
    "maverick-mcp": {
      "serverUrl": "http://localhost:8000/sse"
    }
  }
}

Config Location: Windsurf → Settings → Advanced Settings → MCP Servers

Why mcp-remote is Needed

The mcp-remote tool bridges the gap between STDIO-only clients (like Claude Desktop) and HTTP/SSE servers. Without it, these clients cannot connect to remote MCP servers:

Without mcp-remote: Client tries STDIO → Server expects HTTP → Connection fails
With mcp-remote: Client uses STDIO → mcp-remote converts to HTTP → Server receives HTTP → Success

Available Tools

MaverickMCP provides 29+ financial analysis tools organized into focused categories, with access to pre-seeded S&P 500 screening data:

Development Commands

# Start the server (one command!)
make dev

# Alternative startup methods
./scripts/start-backend.sh --dev    # Script-based startup
./tools/fast_dev.sh                 # Ultra-fast startup (< 3 seconds)
uv run python tools/hot_reload.py   # Auto-restart on file changes

# Server will be available at:
# - HTTP endpoint: http://localhost:8000/mcp (recommended for Claude Desktop via mcp-remote)
# - SSE endpoint: http://localhost:8000/sse (legacy compatibility)
# - Health check: http://localhost:8000/health

Testing

# Quick test commands
make test              # Run unit tests (5-10 seconds)
make test-specific TEST=test_name  # Run specific test
make test-watch        # Auto-run tests on file changes

# Using uv (recommended)
uv run pytest                 # Unit tests only
uv run pytest --cov=maverick_mcp  # With coverage
uv run pytest -m ""           # All tests (requires PostgreSQL/Redis)

# Alternative: Direct pytest (if activated in venv)
pytest                 # Unit tests only
pytest --cov=maverick_mcp  # With coverage
pytest -m ""           # All tests (requires PostgreSQL/Redis)

Code Quality

# Quick quality commands
make lint              # Check code quality (ruff)
make format            # Auto-format code (ruff)
make typecheck         # Run type checking (ty)

# Using uv (recommended)
uv run ruff check .    # Linting
uv run ruff format .   # Formatting
uv run ty check .      # Type checking (Astral's modern type checker)

# Alternative: Direct commands (if activated in venv)
ruff check .           # Linting
ruff format .          # Formatting
ty check .             # Type checking

# Ultra-fast one-liner (no installation needed)
uvx ty check .         # Run ty directly without installing

Configuration

Configure MaverickMCP via .env file or environment variables:

Essential Settings:

REDIS_HOST, REDIS_PORT - Redis cache (optional, defaults to localhost:6379)
DATABASE_URL - PostgreSQL connection or sqlite:///maverick_mcp.db for SQLite (default)
LOG_LEVEL - Logging verbosity (INFO, DEBUG, ERROR)
S&P 500 data automatically seeds on first startup

Required API Keys:

TIINGO_API_KEY - Stock data provider (free tier available at tiingo.com)

Optional API Keys:

OPENAI_API_KEY - For AI-powered analysis features
ANTHROPIC_API_KEY - Alternative LLM provider
FRED_API_KEY - Federal Reserve economic data

Performance:

CACHE_ENABLED=true - Enable Redis caching
CACHE_TTL_SECONDS=3600 - Cache duration

Tools

MaverickMCP provides 29+ financial analysis tools organized by category, with pre-seeded S&P 500 data:

Stock Data Tools

fetch_stock_data - Get historical stock data with intelligent caching
fetch_stock_data_batch - Fetch data for multiple tickers simultaneously
get_news_sentiment - Analyze news sentiment for any ticker
clear_cache / get_cache_info - Cache management utilities

Technical Analysis Tools

get_rsi_analysis - RSI calculation with buy/sell signals
get_macd_analysis - MACD analysis with trend identification
get_support_resistance - Identify key price levels
get_full_technical_analysis - Comprehensive technical analysis
get_stock_chart_analysis - Visual chart generation

Portfolio Tools

risk_adjusted_analysis - Risk-based position sizing
compare_tickers - Side-by-side ticker comparison
portfolio_correlation_analysis - Correlation matrix analysis

Stock Screening Tools (Pre-seeded with S&P 500)

get_maverick_stocks - Bullish momentum screening from 520 S&P 500 stocks
get_maverick_bear_stocks - Bearish setup identification from pre-analyzed data
get_trending_breakout_stocks - Strong uptrend phase screening with supply/demand analysis
get_all_screening_recommendations - Combined screening results across all strategies
Database includes comprehensive screening data updated regularly

Market Data Tools

Market overview, sector performance, earnings calendars
Economic indicators and Federal Reserve data
Real-time market movers and sentiment analysis

Resources

stock://{ticker} - Latest year of stock data
stock://{ticker}/{start_date}/{end_date} - Custom date range
stock_info://{ticker} - Basic stock information

Prompts

stock_analysis(ticker) - Comprehensive stock analysis prompt
market_comparison(tickers) - Compare multiple stocks
portfolio_optimization(tickers, risk_profile) - Portfolio optimization guidance

Docker (Optional)

For containerized deployment:

# Copy and configure environment
cp .env.example .env

# Using uv in Docker (recommended for faster builds)
docker build -t maverick_mcp .
docker run -p 8000:8000 --env-file .env maverick_mcp

# Or start with docker-compose
docker-compose up -d

Note: The Dockerfile uses uv for fast dependency installation and smaller image sizes.

Troubleshooting

# Common development issues
make tail-log          # View server logs
make stop              # Stop services if ports are in use
make clean             # Clean up cache files

# Quick fixes:
# Port 8000 in use → make stop
# Redis connection refused → brew services start redis
# Tests failing → make test (unit tests only)
# Slow startup → ./tools/fast_dev.sh
# Missing S&P 500 data → uv run python scripts/seed_sp500.py

Extending MaverickMCP

Add custom financial analysis tools with simple decorators:

@mcp.tool()
def my_custom_indicator(ticker: str, period: int = 14):
    """Calculate custom technical indicator."""
    # Your analysis logic here
    return {"ticker": ticker, "signal": "buy", "confidence": 0.85}

@mcp.resource("custom://analysis/{ticker}")
def custom_analysis(ticker: str):
    """Custom analysis resource."""
    # Your resource logic here
    return f"Custom analysis for {ticker}"

Development Tools

Quick Development Workflow

make dev               # Start everything
make stop              # Stop services
make tail-log          # Follow server logs
make test              # Run tests quickly
make experiment        # Test custom analysis scripts

Smart Error Handling

MaverickMCP includes helpful error diagnostics:

DataFrame column case sensitivity → Shows correct column name
Connection failures → Provides specific fix commands
Import errors → Shows exact install commands
Database issues → Suggests SQLite fallback

Fast Development Options

Hot Reload: uv run python tools/hot_reload.py - Auto-restart on changes
Fast Startup: ./tools/fast_dev.sh - < 3 second startup
Quick Testing: uv run python tools/quick_test.py --test stock - Test specific features
Experiment Harness: Drop .py files in tools/experiments/ for auto-execution

Performance Features

Parallel Screening: 4x faster stock analysis with ProcessPoolExecutor
Smart Caching: @quick_cache decorator for instant re-runs
Optimized Tests: Unit tests complete in 5-10 seconds

Getting Help

For issues or questions:

📖 Check Documentation: Start with this README and CLAUDE.md
🔍 Search Issues: Look through existing GitHub issues
🐛 Report Bugs: Create a new issue with details
💡 Request Features: Suggest improvements via GitHub issues
🤝 Contribute: See our Contributing Guide for development setup

Recent Updates

Personal Use Optimization

No Authentication Required: Removed all authentication/billing complexity for personal use
Pre-seeded S&P 500 Database: 520 stocks with comprehensive screening recommendations
Simplified Architecture: Clean, focused codebase for core stock analysis functionality
Multi-Transport Support: HTTP, SSE, and STDIO for all MCP clients

Development Experience Improvements

Comprehensive Makefile: One command (make dev) starts everything including database seeding
Smart Error Handling: Automatic fix suggestions for common issues
Fast Development: < 3 second startup with ./tools/fast_dev.sh
Parallel Processing: 4x speedup for stock screening operations
Enhanced Tooling: Hot reload, experiment harness, quick testing

Technical Improvements

Modern Tooling: Migrated to uv and ty for faster dependency management and type checking
Market Data: Improved fallback logic and async support
Caching: Smart Redis caching with graceful in-memory fallback
Database: SQLite default with PostgreSQL option for enhanced performance

Acknowledgments

MaverickMCP builds on these excellent open-source projects:

FastMCP - MCP framework powering the server
yfinance - Market data access
TA-Lib - Technical analysis indicators
pandas & NumPy - Data analysis
FastAPI - Modern web framework
The entire Python open-source community

License

MIT License - see LICENSE file for details. Free to use for personal and commercial purposes.

Support

If you find MaverickMCP useful:

⭐ Star the repository
🐛 Report bugs via GitHub issues
💡 Suggest features
📖 Improve documentation

Built for traders and investors. Happy Trading!

Disclaimer

_{This software is for educational and informational purposes only. It is NOT financial advice.}

_{Investment Risk Warning: Past performance does not guarantee future results. All investments carry risk of loss, including total loss of capital. Technical analysis and screening results are not predictive of future performance. Market data may be delayed, inaccurate, or incomplete.}

_{No Professional Advice: This tool provides data analysis, not investment recommendations. Always consult with a qualified financial advisor before making investment decisions. The developers are not licensed financial advisors or investment professionals. Nothing in this software constitutes professional financial, investment, legal, or tax advice.}

_{Data and Accuracy: Market data provided by third-party sources (Tiingo, Yahoo Finance, FRED). Data may contain errors, delays, or omissions. Technical indicators are mathematical calculations based on historical data. No warranty is made regarding data accuracy or completeness.}

_{Regulatory Compliance: US Users - This software is not registered with the SEC, CFTC, or other regulatory bodies. International Users - Check local financial software regulations before use. Users are responsible for compliance with all applicable laws and regulations. Some features may not be available in certain jurisdictions.}

_{Limitation of Liability: Developers disclaim all liability for investment losses or damages. Use this software at your own risk. No guarantee is made regarding software availability or functionality.}

_{By using MaverickMCP, you acknowledge these risks and agree to use the software for educational purposes only.}