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.

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
Advanced Backtesting: VectorBT-powered engine with 15+ built-in strategies and ML algorithms
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)
TA-Lib: Technical analysis library for advanced indicators
Redis (optional, for enhanced caching)
PostgreSQL or SQLite (optional, for data persistence)

Installing TA-Lib

TA-Lib is required for technical analysis calculations.

macOS and Linux (Homebrew):

brew install ta-lib

Windows (Multiple Options):

Option 1: Conda/Anaconda (Recommended - Easiest)

conda install -c conda-forge ta-lib

Option 2: Pre-compiled Wheels

Download the appropriate wheel for your Python version from:
- cgohlke/talib-build releases
- Choose the file matching your Python version (e.g., TA_Lib-0.4.28-cp312-cp312-win_amd64.whl for Python 3.12 64-bit)
Install using pip:

pip install path/to/downloaded/TA_Lib-X.X.X-cpXXX-cpXXX-win_amd64.whl

Option 3: Alternative Pre-compiled Package

pip install TA-Lib-Precompiled

Option 4: Build from Source (Advanced) If other methods fail, you can build from source:

Install Microsoft C++ Build Tools
Download and extract ta-lib C library to C:\ta-lib
Build using Visual Studio tools
Run pip install ta-lib

Verification: Test your installation:

python -c "import talib; print(talib.__version__)"

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:8003/mcp/
# - SSE endpoint: http://localhost:8003/sse/
# - 520 S&P 500 stocks pre-loaded with screening data

Connect to Claude Desktop

Recommended: STDIO connection

Claude Desktop works best with direct STDIO for local use:

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

[!WARNING] Windows Claude Desktop Users Claude Desktop on Windows currently has a bug where it ignores the "cwd" configuration parameter, which can cause the server to crash with a ModuleNotFoundError when running via uv.

To bypass this, wrap the command in cmd.exe to force the directory change:
"maverick-mcp": {
  "command": "cmd.exe",
  "args": [
    "/c",
    "cd /d C:\\Path\\To\\maverick-mcp && uv run python -m maverick_mcp.api.server --transport stdio"
  ]
}

Config File Location:

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

Always restart Claude Desktop after making configuration changes.

Alternative: Streamable HTTP with mcp-remote

Start the server:

make dev

Then configure a bridge:

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:8003/mcp/"]
    }
  }
}

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

Claude Desktop Configuration

Config Location:

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

Cursor IDE

Option 1: Streamable HTTP bridge:

{
  "mcpServers": {
    "maverick-mcp": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:8003/mcp/"]
    }
  }
}

Option 2: Legacy direct SSE:

{
  "mcpServers": {
    "maverick-mcp": {
      "url": "http://localhost:8003/sse/"
    }
  }
}

Config Location: Cursor → Settings → MCP Servers

Claude Code CLI - All Transports

HTTP transport:

claude mcp add --transport http maverick-mcp http://localhost:8003/mcp/

SSE transport (legacy/debug):

claude mcp add --transport sse maverick-mcp http://localhost:8003/sse/

STDIO transport:

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

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