{"id":"maverick-mcp","name":"Maverick MCP","homepage":"https://github.com/wshobson/maverick-mcp","repo_url":"https://github.com/wshobson/maverick-mcp","category":"finance","subcategories":["stock-analysis","technical-analysis","portfolio-optimization","backtesting"],"tags":["mcp-server","stock-analysis","technical-indicators","backtesting","portfolio","fastmcp","vectorbt","redis","tiingo","financial-data"],"what_it_does":"Personal stock analysis MCP server built on FastMCP 2.0 providing 39+ financial tools including technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands), VectorBT-powered backtesting with 15+ strategies and ML algorithms, stock screening, portfolio optimization, and AI-powered research agents. Pre-seeded with 520 S&P 500 stocks.","use_cases":["Personal stock screening and analysis via Claude Desktop","Backtesting trading strategies with historical data","Technical indicator computation across portfolios","Portfolio correlation analysis and optimization","AI-powered financial research with multiple LLM backends"],"not_for":["Production trading systems or automated order execution","Institutional-grade compliance or audit requirements","Real-time streaming market data","Cryptocurrency analysis"],"best_when":"You want comprehensive personal stock analysis tools directly accessible from Claude Desktop or other MCP clients, with backtesting capabilities and no commercial platform subscription fees.","avoid_when":"You need production-grade trading infrastructure, real-time data feeds, or regulatory-compliant financial tools. Also avoid if you don't want to manage API keys for data providers.","alternatives":["financemcp","alpaca-mcp-server","yahoo-finance-mcp"],"af_score":65.0,"security_score":60.0,"reliability_score":null,"package_type":"mcp_server","discovery_source":["github"],"priority":"low","status":"evaluated","version_evaluated":"unknown","last_evaluated":"2026-03-01T09:50:05.832349+00:00","performance":{"latency_p50_ms":null,"latency_p99_ms":null,"uptime_sla_percent":null,"rate_limits":null,"data_source":"llm_estimated","measured_on":null}}