MaverickMCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_LEVEL | No | Logging verbosity (INFO, DEBUG, ERROR) | |
| REDIS_HOST | No | Redis cache host | |
| REDIS_PORT | No | Redis cache port | |
| DATABASE_URL | No | PostgreSQL connection URL or SQLite path | |
| FRED_API_KEY | No | Federal Reserve economic data API key | |
| CACHE_ENABLED | No | Enable Redis caching | |
| OPENAI_API_KEY | No | OpenAI API key for AI-powered analysis features | |
| TIINGO_API_KEY | Yes | Stock data provider API key (free tier available at tiingo.com) | |
| ANTHROPIC_API_KEY | No | Anthropic API key as alternative LLM provider | |
| CACHE_TTL_SECONDS | No | Cache duration in seconds |
Capabilities
Features and capabilities supported by this server
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| technical_get_rsi_analysisC | Get RSI analysis for a given ticker. |
| technical_get_macd_analysisC | Get MACD analysis for a given ticker. |
| technical_get_support_resistanceC | Get support and resistance levels for a given ticker. |
| technical_get_full_technical_analysisB | Get comprehensive technical analysis for a given ticker with enhanced logging and timeout handling. This enhanced version provides:
|
| technical_get_stock_chart_analysisC | Generate a comprehensive technical analysis chart with enhanced error handling. This enhanced version provides:
|
| screening_get_maverick_stocksB | Get top Maverick stocks from the screening results. DISCLAIMER: Stock screening results are for educational and research purposes only. This is not investment advice. Past performance does not guarantee future results. Always conduct thorough research and consult financial professionals before investing. The Maverick screening strategy identifies stocks with:
|
| screening_get_maverick_bear_stocksA | Get top Maverick Bear stocks from the screening results. DISCLAIMER: Bearish screening results are for educational purposes only. This is not advice to sell short or make bearish trades. Short selling involves unlimited risk potential. Always consult financial professionals before trading. The Maverick Bear screening identifies stocks with:
|
| screening_get_supply_demand_breakoutsA | Get stocks showing supply/demand breakout patterns from accumulation. This screening identifies stocks in the demand expansion phase with:
|
| screening_get_all_screening_recommendationsA | Get comprehensive screening results from all strategies. This tool returns the top stocks from each screening strategy:
Returns: Dictionary containing all screening results organized by strategy |
| screening_get_screening_by_criteriaB | Get stocks filtered by specific screening criteria. This tool allows custom filtering across all screening results based on:
|
| portfolio_add_positionB | Add a stock position to your portfolio. This tool adds a new position or increases an existing position in your portfolio. If the ticker already exists, it will average the cost basis automatically. |
| portfolio_get_my_portfolioB | Get your complete portfolio with all positions and performance metrics. This tool retrieves your entire portfolio including:
|
| portfolio_remove_positionA | Remove shares from a position in your portfolio. This tool removes some or all shares of a stock from your portfolio. If no share count is specified, the entire position is removed. |
| portfolio_clear_portfolioA | Clear all positions from your portfolio. CAUTION: This removes all positions from the specified portfolio. This action cannot be undone. |
| portfolio_risk_adjusted_analysisB | Perform risk-adjusted stock analysis with position sizing. DISCLAIMER: This analysis is for educational purposes only and does not constitute investment advice. All investments carry risk of loss. Always consult with qualified financial professionals before making investment decisions. This tool analyzes a stock with risk parameters tailored to different investment styles. It provides:
Portfolio Integration: If you already own this stock, the analysis includes:
The risk_level parameter (0-100) adjusts the analysis from conservative (low) to aggressive (high). |
| portfolio_compare_tickersA | Compare multiple tickers using technical and fundamental metrics. This tool provides side-by-side comparison of stocks including:
Portfolio Integration: If no tickers are provided, automatically compares all positions in your portfolio, making it easy to see which holdings are performing best. |
| portfolio_portfolio_correlation_analysisA | Analyze correlation between multiple securities. DISCLAIMER: This correlation analysis is for educational purposes only. Past correlations do not guarantee future relationships between securities. Always diversify appropriately and consult with financial professionals. This tool calculates the correlation matrix for a portfolio of stocks, helping to identify:
Portfolio Integration: If no tickers are provided, automatically analyzes correlation between all positions in your portfolio, helping you understand diversification and identify concentration risk. |
| data_fetch_stock_dataC | Fetch historical stock data for a given ticker symbol. This is the primary tool for retrieving stock price data. It uses intelligent caching to minimize API calls and improve performance. Updated to use separated services following Single Responsibility Principle. |
| data_fetch_stock_data_batchA | Fetch historical data for multiple tickers efficiently. This tool fetches data for multiple stocks in a single call, which is more efficient than calling fetch_stock_data multiple times. Updated to use separated services following Single Responsibility Principle. |
| data_get_stock_infoB | Get detailed fundamental information about a stock. This tool retrieves comprehensive stock information including:
|
| data_get_adanos_market_sentimentC | Get optional Adanos market sentiment data for stocks. |
| data_get_news_sentimentA | Get news sentiment analysis for a stock using Tiingo News API or LLM analysis. This enhanced tool provides reliable sentiment analysis by:
|
| data_get_cached_price_dataA | Get cached price data directly from the database. This tool retrieves data from the local cache without making external API calls. Useful for checking what data is available locally. |
| data_get_chart_linksA | Provide links to various financial charting websites. This tool generates URLs to popular financial websites where detailed stock charts can be viewed, including:
|
| data_clear_cacheA | Clear cached data for a specific ticker or all tickers. This tool helps manage the local cache by removing stored data, forcing fresh data retrieval on the next request. |
| performance_get_system_performance_healthA | Get comprehensive system performance health report. This tool provides an overall health assessment of the MaverickMCP system, including Redis connectivity, cache performance, database query metrics, and index usage analysis. Use this for general system health monitoring. |
| performance_get_redis_health_statusA | Get Redis connection pool health and performance metrics. This tool provides detailed information about Redis connectivity, connection pool status, operation latency, and basic health tests. Use this when diagnosing Redis-related performance issues. Returns: Redis health status and connection metrics |
| performance_get_cache_performance_statusA | Get cache performance metrics and optimization suggestions. This tool provides cache hit/miss ratios, operation latencies, Redis memory usage, and performance test results. Use this to optimize caching strategies and identify cache bottlenecks. Returns: Cache performance metrics and test results |
| performance_get_database_performance_statusA | Get database query performance metrics and connection pool status. This tool provides database query statistics, slow query detection, connection pool metrics, and database health tests. Use this to identify database performance bottlenecks and optimization opportunities. Returns: Database performance metrics and query statistics |
| performance_analyze_database_index_usageA | Analyze database index usage and provide optimization recommendations. This tool examines database index usage statistics, identifies missing indexes, analyzes table scan patterns, and provides specific recommendations for database performance optimization. Use this for database tuning. Returns: Database index analysis and optimization recommendations |
| performance_optimize_cache_configurationA | Analyze cache usage patterns and recommend optimal configuration. This tool analyzes current cache hit rates, memory usage, and access patterns to recommend optimal TTL values, cache sizes, and configuration settings for maximum performance. Use this for cache tuning. Returns: Cache optimization analysis and recommended settings |
| performance_clear_system_cachesA | Clear specific performance caches for maintenance or testing. This tool allows selective clearing of different cache types:
Use this for cache maintenance, testing, or when stale data is suspected. |
| agents_list_available_agentsA | List all available LangGraph agents and their capabilities. Returns: Information about available agents and personas |
| agents_analyze_market_with_agentC | Analyze market conditions using an AI agent with specified persona and strategy. |
| agents_get_agent_streaming_analysisC | Get streaming analysis from an AI agent for real-time updates. |
| agents_compare_personas_analysisC | Compare analysis results across different investor personas. |
| agents_orchestrated_analysisC | Perform orchestrated multi-agent analysis with intelligent routing. |
| agents_deep_research_financialC | Perform deep financial research on a topic with AI agents. |
| agents_compare_multi_agent_analysisC | Compare analysis results from multiple agent types side by side. |
| research_comprehensive_researchB | Perform comprehensive research on any financial topic using web search and AI analysis. Enhanced features:
|
| research_company_comprehensiveB | Perform comprehensive research on a specific company. Features:
|
| research_analyze_market_sentimentB | Analyze market sentiment for a specific topic or sector. Features:
|
| get_system_healthB | Get comprehensive system health status. Returns detailed information about all system components including:
Returns: Dictionary containing complete system health information |
| get_component_statusB | Get status of a specific component or all components. |
| get_circuit_breaker_statusA | Get status of all circuit breakers. Returns information about circuit breaker states, failure counts, and performance metrics for all external API connections. Returns: Dictionary containing circuit breaker status information |
| get_resource_usageA | Get current system resource usage. Returns information about CPU, memory, disk usage, and other system resources being consumed by the backtesting system. Returns: Dictionary containing resource usage information |
| get_status_dashboardB | Get comprehensive status dashboard data. Returns aggregated health status, performance metrics, alerts, and historical trends for the entire backtesting system. Returns: Dictionary containing complete dashboard information |
| reset_circuit_breakerC | Reset a specific circuit breaker. |
| get_health_historyA | Get historical health data for trend analysis. Returns recent health check history including component status changes, resource usage trends, and system performance over time. Returns: Dictionary containing historical health information |
| run_health_diagnosticsA | Run comprehensive health diagnostics. Performs a complete system health check including all components, circuit breakers, resource usage, and generates a diagnostic report with recommendations. Returns: Dictionary containing diagnostic results and recommendations |
| run_backtestC | Run a VectorBT backtest with specified strategy and parameters. |
| optimize_strategyB | Optimize strategy parameters using VectorBT grid search. |
| walk_forward_analysisB | Perform walk-forward analysis to test strategy robustness. |
| monte_carlo_simulationC | Run Monte Carlo simulation on backtest results. |
| compare_strategiesC | Compare multiple strategies on the same symbol. |
| list_strategiesA | List all available VectorBT strategies with descriptions. Returns: Dictionary of available strategies and their information |
| parse_strategyA | Parse natural language strategy description into VectorBT parameters. |
| backtest_portfolioC | Backtest a strategy across multiple symbols (portfolio). |
| generate_backtest_chartsC | Generate comprehensive charts for a backtest. |
| generate_optimization_chartsC | Generate chart for strategy parameter optimization. |
| run_ml_strategy_backtestC | Run backtest using ML-enhanced strategies. |
| train_ml_predictorC | Train an ML predictor model for trading signals. |
| analyze_market_regimesC | Analyze market regimes for a stock using ML methods. |
| create_strategy_ensembleB | Create and backtest a strategy ensemble across multiple symbols. |
| discover_capabilitiesA | Discover all available capabilities of the MaverickMCP server. This tool provides comprehensive information about:
Use this as your first tool to understand what's available. |
| list_all_strategiesA | List all available backtesting strategies with their parameters. Returns detailed information about each strategy including:
|
| get_strategy_helpB | Get detailed help for a specific strategy. |
| get_decision_logB | Query the agent decision audit trail. Returns recent decision records showing query classifications, agent routing, token usage, cost estimates, and outcomes. |
| get_tool_registry_statusA | Get tool registry status including rate limits and available tool categories. Returns current rate limit usage and configuration for every tool category. Useful for monitoring tool availability and diagnosing rate-limit issues. Returns: Dictionary with rate_limits (current usage) and tool_categories (config). |
| create_signalA | Create a persistent price/indicator alert signal. The condition dict must specify 'indicator' (price, rsi, volume, sma), 'operator' (lt, gt, lte, gte, spike, crosses_above, crosses_below), and 'threshold' (numeric, not needed for spike). |
| update_signalB | Update an existing signal's label, condition, interval, or active status. |
| list_signalsA | List all configured signals, optionally filtering to active-only. |
| delete_signalB | Delete a signal alert by ID. |
| check_signals_nowA | Manually trigger evaluation of all active signals against current market data. Returns a list of evaluation results including which signals triggered. |
| get_market_regimeA | Detect the current market regime (bull, bear, choppy, or transitional) using SPY price data and a composite multi-factor scoring model. |
| get_regime_historyC | Retrieve recorded market regime events from the database. |
| backtest_signalA | Backtest a saved signal definition against historical OHLCV data. Walks the data bar-by-bar through the live signal evaluation engine so the entry/exit edges match what the signal would produce in production. Returns trade list and summary metrics (total return, win rate, Sharpe, max drawdown). |
| get_screening_changesA | Get recent screening changes (symbol entries and exits). Optionally filter by screen_name. Returns up to |
| get_screening_historyB | Get screening run history for a specific symbol — showing each run in which the symbol appeared. Optionally filter by screen_name. |
| schedule_screeningA | Register scheduling intent for a named screen. Records the screen name and interval in the database for future integration with the scheduler. Actual periodic execution is wired during the integration pass. |
| get_screening_pipeline_statusA | Return overall status of the screening pipeline — latest run per screen, result counts, and any configured scheduled jobs. |
| journal_add_tradeA | Add an open trade to the journal. Record the symbol, side (long/short), entry price, and number of shares. Optionally include a rationale, strategy tags, and notes. Entry date defaults to now. |
| journal_close_tradeA | Close an existing open trade by entry ID. Provide the exit price; exit date defaults to now. PnL is automatically computed (long: exit-entry, short: entry-exit). Strategy performance metrics are recomputed for all tags on this trade. |
| journal_list_tradesA | List trades from the journal. Optionally filter by symbol, status (open/closed), or strategy tag. Returns up to |
| get_strategy_performanceA | Return aggregated performance metrics for a strategy tag: win/loss count, total PnL, average win/loss, expectancy, and profit factor. Metrics are based on all closed trades tagged with this strategy. |
| get_strategy_comparisonA | Compare all strategies ranked by expectancy (highest first). Shows win/loss counts, total PnL, and key metrics for each strategy tag. |
| journal_trade_reviewA | Return full details for a trade entry by ID, including all computed metrics. Shows entry/exit prices, PnL, tags, rationale, and notes. |
| watchlist_createA | Create a new named watchlist for tracking ticker symbols. The name must be unique. An optional description can be provided. |
| watchlist_addB | Add a ticker symbol to an existing watchlist. Optional notes can capture the thesis or context for watching the symbol. |
| watchlist_removeC | Remove a ticker symbol from a watchlist. |
| watchlist_briefA | Generate a scored intelligence brief for every symbol on a watchlist. Each entry includes: active signal count, upcoming catalyst flag (within 30 days), days on watchlist, and notes. Results are sorted by active signals descending. |
| get_upcoming_catalystsA | List upcoming catalyst events (earnings, ex-dividend, FDA decisions, etc.) within a given number of days from today. Optionally filter to a specific list of ticker symbols. |
| get_portfolio_risk_dashboardB | Compute a full risk dashboard for the named portfolio. Returns total value, sector concentration, parametric VaR (95 and 99 confidence), and total unrealised P&L. |
| get_position_risk_checkA | Pre-trade risk check: shows how adding a new position would affect portfolio risk. Returns current vs projected metrics including sector concentration and VaR. |
| get_regime_adjusted_sizingA | Calculate position size adjusted for current market regime. Detects regime from SPY data, then scales risk percentage accordingly: bull = full risk, choppy/transitional = 75%, bear = 50%. |
| get_risk_alertsA | Generate current risk alerts for the named portfolio. Checks for sector concentration (>30% warning, >50% critical), oversized positions (>20%), and portfolio drawdown (>10% loss). |
| get_user_portfolio_summaryC | Get basic portfolio summary and stock analysis capabilities. Returns available features and sample stock data. |
| get_watchlistC | Get sample watchlist with real-time stock data. Provides stock data for popular tickers to demonstrate functionality. |
| get_market_overviewB | Get comprehensive market overview including indices, sectors, and market breadth. Provides full market data without restrictions. |
| get_economic_calendarC | Get upcoming economic events and indicators. Provides full access to economic calendar data. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| backtest_strategy_guide | Guide for running backtesting strategies. |
| ml_strategy_examples | Examples of ML strategy usage. |
| optimization_guide | Guide for parameter optimization. |
| available_tools_summary | Summary of all available MCP tools. |
| troubleshooting_guide | Troubleshooting common issues. |
| quick_start | Quick start guide for new users. |
| strategy_reference | Complete strategy reference with all parameters. |
| technical_analysis | Generate a comprehensive technical analysis prompt for a stock. |
| stock_screening_report | Generate a stock screening report based on different strategies. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| list_strategies_resource | List of all available backtesting strategies with parameters. |
| tool_categories_resource | Categorized list of all available MCP tools. |
| backtesting_examples_resource | Practical examples of using backtesting tools. |
| recent_signal_events | Most recent in-memory signal events captured by the resource notifier. Reads from the ``MCPResourceNotifier`` registered with the service registry under ``"signal_notifiers"`` during server startup. The buffer is bounded (default 100 events) and is in-process only — restarting the server clears it. Use ``list_signal_events`` for the persistent DB-backed history. Returns an empty list with a note when notifiers are disabled (``MAVERICK_SIGNAL_MCP_RESOURCE=0``) or the registry has no ``signal_notifiers`` entry — the resource always succeeds. |
| health_resource | Enhanced comprehensive health check endpoint. Provides detailed system health including: - Component status (database, cache, external APIs) - Circuit breaker states - Resource utilization - Performance metrics Financial Disclaimer: This health check is for system monitoring only and does not provide any investment or financial advice. |
| status_dashboard_resource | Comprehensive status dashboard with real-time metrics. Provides aggregated health status, performance metrics, alerts, and historical trends for the backtesting system. |
| performance_dashboard | Performance metrics dashboard showing backtesting system health. Provides real-time performance metrics, resource usage, and operational statistics for the backtesting infrastructure. |
| portfolio_holdings_resource | Get your current portfolio holdings as an MCP resource. This resource provides AI-enriched context about your portfolio for Claude to use in conversations. It includes all positions with current prices and P&L calculations. Returns: JSON string containing portfolio holdings with performance metrics |
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