Structured-Products-MCP-Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CACHE_DEFAULT_TTL | No | Default cache TTL in milliseconds (5 minutes) | 300000 |
| CACHE_MAX_ENTRIES | No | Maximum number of cache entries | 1000 |
| VOLATILITY_CACHE_TTL | No | Volatility cache TTL in milliseconds (1 hour) | 3600000 |
| ALPHA_VANTAGE_API_KEY | No | Alpha Vantage API key (optional, for real market data) | |
| MARKET_DATA_CACHE_TTL | No | Market data cache TTL in milliseconds (5 minutes) | 300000 |
| ALPHA_VANTAGE_BASE_URL | No | Alpha Vantage base URL | https://www.alphavantage.co/query |
| TREASURY_RATE_CACHE_TTL | No | Treasury rate cache TTL in milliseconds (24 hours) | 86400000 |
| ALPHA_VANTAGE_RATE_LIMIT_CALLS | No | Alpha Vantage rate limit calls per window | 5 |
| ALPHA_VANTAGE_RATE_LIMIT_WINDOW | No | Alpha Vantage rate limit window in milliseconds (1 minute) | 60000 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_payoff_diagramA | Generate payoff diagrams for structured products like autocallables, barrier options, and custom structures |
| run_monte_carlo_simulationC | Run Monte Carlo simulations for exotic payoffs and risk analysis |
| stress_test_scenariosC | Perform stress testing across different market conditions with real market data integration |
| optimize_structureB | Find optimal strikes and barriers for structured products with real market data integration |
| cache_statusB | Get comprehensive cache performance metrics and status |
| test_cacheC | Test cache performance with timing comparisons across multiple API calls |
| build_portfolioC | Build and optimize multi-asset portfolios using modern portfolio theory with real market data |
| analyze_stockB | Comprehensive stock analysis with technical indicators, fundamentals, and investment signals |
| analyze_advanced_riskC | Advanced portfolio risk analysis with FinQuant-inspired metrics including Sortino ratio, Treynor ratio, downside deviation, and comprehensive risk decomposition |
| analyze_risk_attributionC | Portfolio risk attribution analysis - decompose portfolio risk by factors including market, sector, and specific risks with correlation analysis |
| optimize_black_littermanB | Black-Litterman portfolio optimization combining market equilibrium with investor views for more realistic and stable portfolio allocations |
| create_black_litterman_viewsB | Interactive guide for creating Black-Litterman investment views with examples and best practices |
| optimize_risk_parityB | Risk Parity portfolio optimization where each asset contributes equally to portfolio risk, providing better diversification than equal-weight portfolios |
| compare_risk_parity_methodsA | Compare different Risk Parity optimization methods (Standard, Constrained, Hierarchical) side-by-side with detailed analysis |
| run_backtesting_analysisC | Comprehensive backtesting analysis with transaction costs, rebalancing strategies, and performance metrics |
| run_walk_forward_testB | Walk-forward optimization testing to validate strategy robustness over time |
| run_strategy_comparisonA | Comprehensive comparison of multiple portfolio strategies with backtesting, walk-forward analysis, and Monte Carlo validation |
| run_monte_carlo_robustness_testC | Monte Carlo robustness testing for portfolio strategies with confidence intervals and parameter sensitivity analysis |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 18 tools
Most tools target distinct actions, but several validation-focused tools (run_backtesting_analysis, run_walk_forward_test, run_strategy_comparison, run_monte_carlo_robustness_test) overlap in purpose and could lead to misselection. The cache tools and structured product tools are clearly distinct.
The majority follow a verb_noun pattern (e.g., run_monte_carlo_simulation, optimize_risk_parity, build_portfolio). Minor deviations like cache_status (no verb) and some longer multi-word names are acceptable, but the overall pattern is predictable.
With 18 tools, the server is on the heavier side. The count is not unreasonable for a broad quant toolkit, but the inclusion of cache utilities and the mismatch with the 'Structured Products' name suggest the scope is too wide and could be trimmed.
The financial tools cover a range of analysis, optimization, and backtesting, but there are gaps: no direct pricing tool for structured products, no dedicated data fetching, and no explicit portfolio performance measurement. The server's stated purpose is only partially fulfilled, and unrelated cache tools signal incomplete domain coverage.