Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Structured-Products-MCP-Server Generate a payoff diagram for an autocallable with 30% barrier."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A comprehensive MCP server for analyzing financial structured products, portfolio optimization, and advanced risk analytics, designed for Claude Desktop integration.
This server provides advanced financial analytics capabilities through Claude Desktop integration. It can:
Analyze Structured Products - Generate payoff diagrams for options, autocallables, and barrier products
Optimize Portfolios - Modern portfolio theory, Black-Litterman, and risk parity optimization
Assess Risk - Advanced risk metrics including VaR, Sortino ratio, and drawdown analysis
Backtest Strategies - Historical testing with walk-forward analysis and Monte Carlo validation
Process Market Data - Real-time market data integration with intelligent caching
Calculate Greeks - Complete sensitivity analysis for options and derivatives
Related MCP server: trading-skills
Install dependencies: npm install
Start server: npm start or node server.js
Development mode: npm run dev (with auto-restart on changes)
Run tests: npm test (executes comprehensive test suite)
Individual test files:
node test-alpha-vantage.js (market data API tests)
node test-cache.js (cache performance tests)
node test-phase2.js (phase 2 tools tests)
The project consists of several key components:
MCP Server Core (server.js) - Main server with 20+ financial analysis tools
Financial Tools (tools/) - Specialized financial analysis implementations
Data Processing (utils/) - Market data integration and caching systems
Service Layer (services/) - High-level financial services
server.js: Main MCP server with 20+ financial analysis tools
tools/: Tool implementations organized by functionality
Financial math core (financial-math.js, monte-carlo.js)
Portfolio optimization (portfolio-optimizer.js, black-litterman-optimizer.js, risk-parity-optimizer.js)
Risk analysis (advanced-risk-analyzer.js, scenario-analysis.js)
Backtesting (backtesting-tools.js)
utils/: Shared utilities and data processing
Market data (alpha-vantage-client.js, market-calculations.js)
Caching (data-cache.js - in-memory cache with TTL)
Analysis engines (backtesting-engine.js, technical-analysis.js)
services/: High-level service layer (market-data.js)
Market Data: Real-time data from Alpha Vantage API with intelligent caching (5min-24hr TTL)
Processing: Financial calculations using mathjs, ml-matrix, and custom algorithms
Caching: Multi-tier caching (market data 5min, volatility 1hr, rates 24hr)
Output: Structured markdown with ASCII visualizations
MCP SDK: @modelcontextprotocol/sdk v1.0.0 for Claude integration
Financial Math: mathjs, ml-matrix, simple-statistics, regression
Market Data: Alpha Vantage API with node-fetch
Caching: Custom in-memory cache with LRU eviction and TTL
generate_payoff_diagram: Payoff analysis for options, autocallables, barriers
run_monte_carlo_simulation: Monte Carlo for exotic derivatives
stress_test_scenarios: Multi-scenario stress testing
optimize_structure: Parameter optimization for structured products
build_portfolio: Modern portfolio theory optimization
optimize_black_litterman: Black-Litterman with investor views
optimize_risk_parity: Equal risk contribution optimization
compare_risk_parity_methods: Method comparison analysis
analyze_advanced_risk: Comprehensive risk metrics (Sortino, Treynor, VaR)
analyze_risk_attribution: Factor-based risk decomposition
analyze_stock: Technical and fundamental analysis
run_backtesting_analysis: Historical strategy testing
run_walk_forward_test: Walk-forward optimization
run_strategy_comparison: Multi-strategy comparison
run_monte_carlo_robustness_test: Robustness validation
cache_status: Cache performance metrics
test_cache: Cache timing analysis
Node.js 18+
Alpha Vantage API key (optional, for real market data)
Claude Desktop with MCP support
Clone or download this repository
Install dependencies:
npm installConfigure environment variables (optional):
# Alpha Vantage API (for real market data)
ALPHA_VANTAGE_API_KEY=your_api_key_here
ALPHA_VANTAGE_BASE_URL=https://www.alphavantage.co/query
# Cache Configuration (optional - defaults provided)
CACHE_MAX_ENTRIES=1000
CACHE_DEFAULT_TTL=300000 # 5 minutes
MARKET_DATA_CACHE_TTL=300000 # 5 minutes
VOLATILITY_CACHE_TTL=3600000 # 1 hour
TREASURY_RATE_CACHE_TTL=86400000 # 24 hours
# Rate Limiting (optional)
ALPHA_VANTAGE_RATE_LIMIT_CALLS=5
ALPHA_VANTAGE_RATE_LIMIT_WINDOW=60000 # 1 minuteTest setup: npm test
Add this server to your Claude Desktop MCP configuration:
{
"mcpServers": {
"financial-structured-products": {
"command": "node",
"args": ["/path/to/your/server.js"]
}
}
}Replace /path/to/your/server.js with the actual path to this project's server.js file.
# Test core functionality
npm test
# Test market data integration
node test-alpha-vantage.js
# Test cache performance
node test-cache.js
# Test advanced tools
node test-phase2.js"Analyze AAPL with technical indicators and build an optimal portfolio with MSFT and GOOGL"
"Stress test a barrier option on TSLA using real market volatility"
"Compare risk parity vs mean variance for tech stocks: AAPL, MSFT, GOOGL, AMZN""Generate payoff diagram for autocallable on SPY with 15% coupon and 70% barrier"
"Run Monte Carlo simulation for Asian option with 6-month lookback period"
"Optimize barrier option structure targeting 12% annual return with 0.6 risk tolerance""Build Black-Litterman portfolio with bullish view on AAPL vs MSFT"
"Run walk-forward test on risk parity strategy for diversified portfolio"
"Analyze advanced risk metrics for equal-weight portfolio of dividend stocks"For financial analysis safety, the server includes multiple protection mechanisms:
Input Validation: Comprehensive parameter validation for all financial calculations
Rate Limiting: API call limits to prevent excessive market data requests
Error Handling: Graceful degradation when external services are unavailable
Data Caching: Intelligent caching to reduce API load and improve performance
Numerical Stability: Robust mathematical implementations with overflow protection
Audit Logging: Complete logging of all calculations and market data requests
tools/financial-math.js)Black-Scholes Option Pricing Model
Formula: C = S₀N(d₁) - Ke^(-rT)N(d₂) for calls
Parameters: S₀ (spot price), K (strike), T (time to expiry), r (risk-free rate), σ (volatility)
Implementation: blackScholes(S, K, T, r, sigma, optionType)
Greeks Calculation: Full sensitivity analysis with finite difference methods
Delta: ∂V/∂S (price sensitivity)
Gamma: ∂²V/∂S² (delta sensitivity)
Vega: ∂V/∂σ (volatility sensitivity)
Theta: ∂V/∂T (time decay)
Rho: ∂V/∂r (interest rate sensitivity)
Geometric Brownian Motion (GBM)
Model: dS = μSdt + σSdW (stochastic differential equation)
Discretization: S_{t+Δt} = S_t * exp((r - σ²/2)Δt + σ√Δt * ε)
Implementation: simulateGBM(S0, r, sigma, T, steps)
Applications: Monte Carlo path generation, exotic option pricing
Statistical Distributions
Normal CDF/PDF: Error function approximation with Abramowitz-Stegun algorithm
Box-Muller Transform: randomNormal() for Gaussian random number generation
Implementation: Custom functions for numerical accuracy
tools/monte-carlo.js)Advanced Monte Carlo Simulation
Path Generation: Multi-step GBM simulation with configurable time steps
Payoff Structures: Support for exotic derivatives (Asian, Barrier, Autocallable, Lookback)
Variance Reduction: Antithetic variates and control variates (planned)
Greek Estimation: Finite difference method with optimal bump sizes
Specialized Product Pricing
Autocallable Notes: Early redemption with barrier observation
Barrier Options: Down-and-out/in with continuous monitoring
Asian Options: Arithmetic average price options
Rainbow Options: Multi-asset best-of/worst-of structures
Risk Assessment Integration
Value at Risk (VaR): Historical and parametric methods at 95%/99% confidence
Expected Shortfall: Conditional VaR calculation
Maximum Drawdown: Peak-to-trough analysis
Barrier Breach Analysis: Knock-out probability estimation
utils/portfolio-math.js)Modern Portfolio Theory (Markowitz)
Mean-Variance Optimization: min w'Σw subject to w'μ = μₚ, w'1 = 1
Efficient Frontier: Parametric optimization across return-risk spectrum
Maximum Sharpe Ratio: Tangency portfolio calculation
Implementation: Matrix operations with ml-matrix library
Black-Litterman Model
Equilibrium Returns: π = λΣw_market (CAPM-based implied returns)
Bayesian Update: μ_BL = [(τΣ)⁻¹ + P'Ω⁻¹P]⁻¹[(τΣ)⁻¹π + P'Ω⁻¹Q]
View Matrix: P (picking matrix), Q (view returns), Ω (view uncertainty)
Parameters: τ (prior uncertainty), λ (risk aversion coefficient)
Risk Parity Optimization
Equal Risk Contribution: Target RC_i = 1/n for all assets
Risk Contribution: RC_i = w_i * (Σw)_i / (w'Σw)
Optimization Method: Spinu (2013) iterative rebalancing algorithm
Constrained Version: Weight bounds with projection methods
Hierarchical Approach: Correlation-based clustering with inverse variance allocation
tools/advanced-risk-analyzer.js)Downside Risk Measures
Sortino Ratio: (r_p - r_f) / DD where DD = √E[min(r_t - τ, 0)²]
Downside Deviation: Semi-standard deviation below target return
Upside Potential Ratio: Upside potential / downside deviation
Semi-variance: Variance of negative returns only
Drawdown Analysis
Maximum Drawdown: max_t[(peak_t - trough_t) / peak_t]
Calmar Ratio: Annual return / maximum drawdown
Recovery Period: Time from peak to recovery
Peak-to-trough Detection: Rolling maximum analysis
Value at Risk Models
Historical VaR: Empirical quantile method
Parametric VaR: Assumes normal distribution with z-score multiplier
Expected Shortfall: E[r | r ≤ VaR] (coherent risk measure)
Confidence Levels: 95% and 99% standard implementations
Beta and Systematic Risk
Portfolio Beta: β_p = Cov(r_p, r_m) / Var(r_m)
Treynor Ratio: (r_p - r_f) / β_p (systematic risk-adjusted return)
Information Ratio: α_p / TE where TE is tracking error
Tracking Error: √Var(r_p - r_b) (active risk)
utils/technical-analysis.js)Moving Average Systems
Simple Moving Average (SMA): SMA_n = Σp_i / n
Exponential Moving Average (EMA): EMA_t = α*p_t + (1-α)*EMA_{t-1}
Bollinger Bands: SMA ± k*σ where σ is rolling standard deviation
MACD: EMA_12 - EMA_26 with signal line EMA_9
Momentum Indicators
Relative Strength Index (RSI): RSI = 100 - 100/(1 + RS) where RS = avg_gain/avg_loss
Stochastic Oscillator: %K = (C - L14)/(H14 - L14) * 100
Rate of Change (ROC): (P_t - P_{t-n})/P_{t-n} * 100
Volatility Measures
Historical Volatility: σ = √(252 * Var(log returns)) (annualized)
Parkinson Estimator: Uses high-low-open-close data for efficiency
Rolling Volatility: Time-varying estimates with configurable windows
utils/backtesting-engine.js)Strategy Testing Framework
Walk-Forward Analysis: Rolling optimization and out-of-sample testing
Monte Carlo Robustness: Parameter sensitivity via bootstrap sampling
Multi-Strategy Comparison: Risk-adjusted performance metrics
Transaction Cost Integration: Bid-ask spreads and impact costs
Performance Attribution
Factor Decomposition: Systematic vs. specific returns
Style Analysis: Sharpe (1992) returns-based attribution
Risk Attribution: Contribution to portfolio variance by factor
Active Share: Σ|w_p - w_b|/2 (portfolio vs. benchmark differences)
.
├── .env # API keys (not in git)
├── .gitignore # Git ignore rules
├── README.md # This file
├── package.json # Node.js dependencies
├── server.js # Main MCP server
├── tools/ # Financial analysis tools
│ ├── financial-math.js # Core financial mathematics
│ ├── monte-carlo.js # Monte Carlo simulations
│ ├── portfolio-optimizer.js # Portfolio optimization
│ ├── black-litterman-optimizer.js # Black-Litterman model
│ ├── risk-parity-optimizer.js # Risk parity optimization
│ ├── advanced-risk-analyzer.js # Advanced risk metrics
│ ├── backtesting-tools.js # Backtesting framework
│ └── scenario-analysis.js # Stress testing
├── utils/ # Shared utilities
│ ├── alpha-vantage-client.js # Market data client
│ ├── data-cache.js # Caching system
│ ├── portfolio-math.js # Portfolio mathematics
│ ├── technical-analysis.js # Technical indicators
│ └── backtesting-engine.js # Backtesting engine
├── services/ # High-level services
│ └── market-data.js # Market data service
└── test*.js # Test suitesMCP SDK: @modelcontextprotocol/sdk v1.0.0 for Claude integration
Financial Math: mathjs, ml-matrix, simple-statistics, regression
Market Data: Alpha Vantage API with node-fetch
Caching: Custom in-memory cache with LRU eviction and TTL
Numerical Methods: Matrix operations, optimization, root finding
Performance: Multi-tier caching, rate limiting, async processing
Want to add new capabilities? The modular architecture makes it easy:
New Financial Tools: Add implementations to tools/ directory
Custom Analysis: Extend utils/ with new calculation methods
Enhanced Caching: Improve caching strategies in utils/data-cache.js
New Data Sources: Add market data providers to utils/alpha-vantage-client.js
The server will automatically use your new capabilities through the MCP interface.
test.js: Core functionality tests (payoff diagrams, Monte Carlo, optimization)
test-alpha-vantage.js: Market data API connectivity and rate limiting
test-cache.js: Cache performance benchmarking and timing analysis
test-phase2.js: Advanced tools (portfolio optimization, risk analytics)
API Issues: Check ALPHA_VANTAGE_API_KEY configuration and rate limits
Cache Problems: Use cache_status tool to monitor hit/miss ratios
Performance: Run test_cache to benchmark API vs cached response times
Calculations: Verify financial math with known option values in test.js
Requires Node.js 18+ (specified in package.json engines)
Works with or without Alpha Vantage API (degrades gracefully)
All dependencies are production-ready packages (mathjs, ml-matrix, etc.)
This tool is for educational and analysis purposes only
Not intended for actual trading or investment decisions
Always consult with qualified financial professionals for investment advice
Market data is provided for informational purposes only
Past performance does not guarantee future results
This is a learning project. Use it to understand financial analytics and MCP integration.
Advanced portfolio risk analysis with FinQuant-inspired metrics including Sortino ratio, Treynor ratio, downside deviation, and comprehensive risk decomposition
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Array of stock symbols for portfolio analysis (e.g., ['AAPL', 'MSFT', 'GOOGL']) | |
| weights | No | Portfolio weights for each symbol (must sum to 1). If not provided, equal weights are used | |
| risk_free_rate | No | Risk-free rate for Sharpe/Sortino calculations. If not provided, fetches current Treasury rate | |
| rolling_window | No | Rolling window size for rolling risk analysis (default: 30 days) | |
| analysis_period | No | Number of trading days for analysis (default: 252 = 1 year) | |
| use_market_data | No | Use real market data for analysis | |
| benchmark_symbol | No | Benchmark symbol for relative performance analysis (e.g., 'SPY' for S&P 500) | SPY |
| confidence_levels | No | Confidence levels for VaR calculation (e.g., [0.95, 0.99]) | |
| include_attribution | No | Include risk attribution and factor analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it only lists metrics. It doesn't state whether the tool fetches live data, performs calculations locally, or what data requirements exist. No mention of side effects, permissions, or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One concise sentence with specific examples of metrics. Avoids fluff, though it could be slightly longer to include usage context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 9-parameter complexity and no output schema, the description offers only a metrics list. It lacks usage context, return value expectations, and guidance on parameter interactions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters with descriptions, so baseline 3 applies. The description adds high-level context about the analysis type but doesn't directly enhance parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states an advanced portfolio risk analysis tool with concrete metric names (Sortino, Treynor, downside deviation). It distinguishes from generic tools but doesn't explicitly differentiate from sibling 'analyze_risk_attribution' which may overlap in 'risk decomposition'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to choose this tool over siblings like 'analyze_risk_attribution' or 'optimize_risk_parity'. The description only states capabilities without context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Portfolio risk attribution analysis - decompose portfolio risk by factors including market, sector, and specific risks with correlation analysis
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Array of stock symbols for attribution analysis (e.g., ['AAPL', 'MSFT', 'GOOGL']) | |
| weights | No | Portfolio weights for each symbol (must sum to 1). If not provided, equal weights are used | |
| analysis_period | No | Number of trading days for analysis (default: 252 = 1 year) | |
| use_market_data | No | Use real market data for attribution analysis | |
| attribution_factors | No | Risk attribution factors to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose whether the operation is read-only, whether it fetches external market data (though use_market_data parameter implies this), or what the return format is. The verb 'decompose' hints at analysis but lacks explicit safety or side-effect information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the tool's primary purpose. It is reasonably concise, though the phrase 'Portfolio risk attribution analysis' is slightly redundant with the tool name and could be streamlined.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters, no output schema, and no annotations, the description is too brief. It does not explain what the analysis returns, any assumptions, or data requirements. This is insufficient for an agent to fully anticipate the tool's behavior without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the parameters are already well-documented. The description adds minimal value by mentioning market, sector, and specific risks, which aligns with the attribution_factors parameter, but it doesn't explain parameter behavior beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool decomposes portfolio risk by market, sector, and specific factors, which is a specific verb+resource combination. It distinguishes itself from siblings like stress_test_scenarios or optimize_risk_parity by focusing on attribution, but it could be more explicit about the output or analysis type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives among the many sibling risk and portfolio analysis tools. No prerequisites, exclusions, or intended use cases are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive stock analysis with technical indicators, fundamentals, and investment signals
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Stock symbol to analyze (e.g., 'AAPL', 'TSLA') | |
| analysis_period | No | Number of days for technical analysis (default: 90) | |
| signal_strength | No | Required signal strength for buy/sell recommendations | medium |
| include_technical | No | Include technical analysis (moving averages, RSI, MACD, Bollinger Bands) | |
| include_fundamentals | No | Include fundamental analysis (P/E, market cap, financials) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose output format, data sources, whether recommendations are returned, or any limitations. It only names analysis categories, leaving significant behavioral uncertainty for a tool with no structural annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded declarative sentence with no filler or redundant restatement of the tool name. Every word contributes to conveying the tool's scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, yet the description does not explain the return value or how the analysis is presented. It also does not mention preconditions or limitations, so overall invocation context is incomplete despite complete parameter documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides descriptions for all five parameters, so the baseline is 3. The tool description adds no parameter-level meaning beyond what the schema already states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb 'analyze' with a clear resource 'stock' and enumerates the content areas (technical indicators, fundamentals, investment signals). This clearly differentiates the tool from sibling tools focused on specific strategies like Monte Carlo simulation or backtesting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided about when to use this tool versus alternatives. Sibling tool names suggest distinct use cases, but the description does not name them or state exclusions, leaving the agent to infer applicability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Build and optimize multi-asset portfolios using modern portfolio theory with real market data
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Array of stock symbols to include in portfolio (e.g., ['AAPL', 'MSFT', 'GOOGL']) | |
| time_horizon | No | Analysis time horizon in trading days (default: 252 = 1 year) | |
| target_return | No | Target annual return (e.g., 0.12 for 12%) | |
| risk_free_rate | No | Risk-free rate for Sharpe ratio calculation (if not provided, fetches Treasury rate) | |
| risk_tolerance | No | Risk tolerance on 0-1 scale (0=very conservative, 1=very aggressive) | |
| use_market_data | No | Use real-time market data for optimization | |
| optimization_method | No | Portfolio optimization method | max_sharpe |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'real market data' suggesting network access, but does not disclose specifics like whether it is read-only, what side effects occur, how data is fetched, or what the output looks like. The description is too high-level to set proper expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that is front-loaded with the main verb ('Build and optimize') and resource ('multi-asset portfolios'). It contains no fluff, but could arguably include a bit more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, no output schema, no annotations), the description is insufficient. It does not explain what the tool returns (e.g., weights, efficient frontier), how parameters like risk_tolerance or target_return interact, or how this tool relates to the broader suite beyond a one-line summary. Users are left without critical context for a complex financial tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides 100% coverage with descriptive parameter details, so the baseline is 3. The description itself adds no additional parameter semantics beyond what the schema already provides, thus it does not exceed the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool builds and optimizes multi-asset portfolios using modern portfolio theory with real market data. This verb+resource combination is specific and distinguishes it from related tools like optimize_black_litterman or optimize_risk_parity, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance is provided. The reference to modern portfolio theory implies a use case, but the description does not tell the user when to prefer this over sibling tools like optimize_black_litterman or optimize_risk_parity, nor does it mention any prerequisites or exclusion scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive cache performance metrics and status
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only status check but never explicitly states safety traits, return format, or any limitations. 'Get' and 'status' suggest non-mutating behavior, but this is not made explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler or redundant explanation. It conveys the core action and resource immediately, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple no-parameter tool with no output schema, so the description is the only source of context. 'Comprehensive cache performance metrics and status' gives a general idea but lacks specifics about what metrics are included or the response structure, leaving some ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema already confirms this with 100% coverage. The description adds no parameter details because none are needed, and the baseline of 4 for a no-parameter tool is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb ('Get') and identifies the resource ('cache performance metrics and status'). It is specific enough to understand the tool's function, though it doesn't explicitly differentiate from the sibling 'test_cache' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives like 'test_cache' or the other analysis tools. The description only states what it does, not the context or conditions for using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare different Risk Parity optimization methods (Standard, Constrained, Hierarchical) side-by-side with detailed analysis
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Array of stock symbols for Risk Parity comparison (e.g., ['AAPL', 'MSFT', 'GOOGL', 'AMZN']) | |
| analysis_period | No | Number of trading days for analysis (default: 252 = 1 year) | |
| use_market_data | No | Use real market data for comparison | |
| include_hierarchical | No | Include Hierarchical Risk Parity in comparison |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only says 'detailed analysis' without specifying what that entails, whether the tool is read-only, what data it uses, or any side effects. This leaves significant behavioral ambiguity for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It efficiently conveys the core action and subject, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema or annotations, the description should clarify what 'detailed analysis' returns or how results are presented. This is missing. Additionally, no context is given about data requirements or how this compares to other analysis tools, leaving gaps for an agent deciding when to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters have meaningful descriptions already. The tool description adds no extra parameter details, but the baseline of 3 is appropriate given the schema carries the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Compare') and specific resource ('Risk Parity optimization methods') with named methods (Standard, Constrained, Hierarchical). This distinguishes it from siblings like optimize_risk_parity or run_strategy_comparison, which focus on single optimization or broader strategy comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: when you need a side-by-side comparison of multiple risk parity methods. However, it does not explicitly mention alternatives or when not to use this tool, such as 'for a single method use optimize_risk_parity'. Clear context but no exclusions warrants a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Interactive guide for creating Black-Litterman investment views with examples and best practices
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | No | Array of stock symbols to create example views for | |
| view_examples | No | Include practical view examples | |
| technical_analysis | No | Include guidance on creating views from technical analysis | |
| fundamental_analysis | No | Include guidance on creating views from fundamental analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only calls the tool an 'interactive guide' without detailing what the user receives, whether any data is modified, or if there are side effects. It doesn't mention output format, interactivity specifics, or any prerequisites, leaving significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that covers the core purpose and highlights examples/best practices. Every word contributes meaningful information without redundancy, and it is appropriately sized for a simple guide tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has four optional params with full schema descriptions but no output schema or annotations, the description is adequate for a guide tool but incomplete. It does not mention what the returned output looks like, how interactive the guide is, or how it differs from optimization tools, leaving clear gaps for the user.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no further meaning about the parameters—it only mentions 'examples and best practices' generally, which aligns with 'view_examples' but adds nothing beyond what the schema already states. The baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it is an 'Interactive guide for creating Black-Litterman investment views with examples and best practices,' which clearly identifies the resource (Black-Litterman views) and the general purpose (a guide for creation). However, it's ambiguous whether the tool itself creates views or simply teaches the user, and it doesn't explicitly distinguish itself from the sibling 'optimize_black_litterman' tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when the user wants to create Black-Litterman views, as it offers 'examples and best practices.' However, there is no explicit statement about when to use this tool versus alternatives like 'optimize_black_litterman' or when not to use it, providing only implied context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate payoff diagrams for structured products like autocallables, barrier options, and custom structures
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | Stock symbol for real market data (e.g., 'AAPL', 'TSLA') | |
| volatility | No | Annual volatility (e.g., 0.25 for 25%). If not provided and use_market_data=true, will be calculated from historical data | |
| price_range | No | Price range for payoff calculation | |
| product_type | Yes | Type of structured product | |
| strike_price | Yes | Strike price of the option/structure | |
| barrier_price | No | Barrier level (for barrier options) | |
| risk_free_rate | No | Risk-free interest rate (e.g., 0.05 for 5%). If not provided and use_market_data=true, will fetch current Treasury rate | |
| time_to_expiry | No | Time to expiry in years | |
| use_market_data | No | Use real-time market data and calculated volatility | |
| underlying_price | Yes | Current underlying asset price |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as whether it fetches market data, what output format it returns, or side effects. The schema hints at market data behavior, but the description itself is silent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence with no fluff. It states the core purpose immediately and every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite a complex tool with 10 parameters and no output schema, the description is a one-liner that doesn't clarify return values, data requirements, or usage examples. The schema provides parameter details, but the description is insufficient for a tool with this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of parameters with descriptions, so baseline is 3. The tool description adds no extra parameter meaning; it only references product types already in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Generate') and resource ('payoff diagrams'), and names example product types, clearly distinguishing it from sibling tools like run_monte_carlo_simulation or analyze_stock.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear context: this tool is for generating payoff diagrams for structured products. It doesn't explicitly state when not to use it or name alternatives, but the purpose is specific enough for an agent to infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Black-Litterman portfolio optimization combining market equilibrium with investor views for more realistic and stable portfolio allocations
| Name | Required | Description | Default |
|---|---|---|---|
| tau | No | Prior uncertainty parameter (typically 0.01-0.1, default: 0.05) | |
| views | No | Array of investment views to incorporate | |
| symbols | Yes | Array of stock symbols for portfolio optimization (e.g., ['AAPL', 'MSFT', 'GOOGL']) | |
| risk_aversion | No | Risk aversion parameter (typical range: 1-10, default: 3) | |
| analysis_period | No | Number of trading days for covariance estimation (default: 252 = 1 year) | |
| use_market_data | No | Use real market data for optimization | |
| view_confidence | No | Confidence levels for each view (overrides individual view confidence) | |
| market_cap_source | No | Source for market capitalization weights | api |
| custom_market_caps | No | Custom market capitalizations when market_cap_source is 'custom' | |
| include_comparison | No | Include comparison with market portfolio | |
| auto_generate_views | No | Automatically generate views from technical/fundamental analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure, but it only states the high-level model and intended benefit. It does not mention how data is fetched, what outputs are returned, or any assumptions or side effects, such as network calls when use_market_data is true.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, a single sentence with no fluff. It front-loads the key concept 'Black-Litterman portfolio optimization' and adds relevant context about combining views with equilibrium.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 11 parameters and no output schema, the description is too sparse. It lacks any details about required inputs, expected outputs, practical use cases, or limitations, making it insufficient for an agent to understand the full scope of the operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in the schema. The description adds no extra parameter context, but the baseline of 3 applies because the schema already provides necessary details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as Black-Litterman portfolio optimization, combining market equilibrium with investor views. This distinguishes it from sibling tools like optimize_risk_parity and create_black_litterman_views by specifying the exact model and purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description does not mention any exclusions or refer to sibling tools, leaving the agent to infer usage solely from the model name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Risk Parity portfolio optimization where each asset contributes equally to portfolio risk, providing better diversification than equal-weight portfolios
| Name | Required | Description | Default |
|---|---|---|---|
| method | No | Risk Parity optimization method | standard |
| symbols | Yes | Array of stock symbols for Risk Parity optimization (e.g., ['AAPL', 'MSFT', 'GOOGL', 'AMZN']) | |
| tolerance | No | Convergence tolerance (default: 1e-6) | |
| max_weights | No | Maximum weight constraints for each asset (e.g., [0.4, 0.4, 0.4, 0.4] for 40% maximum) | |
| min_weights | No | Minimum weight constraints for each asset (e.g., [0.05, 0.05, 0.05, 0.05] for 5% minimum) | |
| max_iterations | No | Maximum optimization iterations (default: 100) | |
| analysis_period | No | Number of trading days for covariance estimation (default: 252 = 1 year) | |
| use_market_data | No | Use real market data for optimization | |
| benchmark_symbol | No | Benchmark symbol for performance comparison | SPY |
| include_comparison | No | Include comparison with equal-weight portfolio |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It explains the theoretical goal (equal risk contribution) but does not disclose practical behaviors: it doesn't state that it uses historical market data when use_market_data=true, what the output looks like (weights, risk metrics), or that it may require internet/data sources. This is a significant gap for a 10-parameter optimization tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence that front-loads the tool's purpose. However, the claim 'better diversification than equal-weight portfolios' is somewhat promotional and not strictly necessary, but overall it's concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 10 parameters including methods, constraints, and data options, this minimal description is insufficient. It doesn't mention there are three optimization methods, that constraints can be applied, that real market data is optional, or what output to expect. No output schema exists, so the description should have explained return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with descriptions, defaults, and examples for all 10 parameters. The tool description adds no parameter-specific semantics beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs Risk Parity portfolio optimization with the goal of equal risk contribution. However, it does not explicitly distinguish itself from sibling tools like compare_risk_parity_methods or optimize_black_litterman, so it lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that you should use this tool when you want risk parity optimization, but it provides no explicit 'when to use vs alternatives' guidance. It doesn't mention exclusions or contrast with other optimization tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find optimal strikes and barriers for structured products with real market data integration
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | Stock symbol for real market data (e.g., 'AAPL', 'TSLA') | |
| volatility | No | Expected volatility. If not provided and use_market_data=true, will be calculated from historical data | |
| product_type | Yes | Type of product to optimize | |
| target_return | Yes | Target annualized return | |
| dividend_yield | No | Dividend yield. If not provided and use_market_data=true, will fetch from company data | |
| risk_free_rate | No | Risk-free rate. If not provided and use_market_data=true, will fetch current Treasury rate | |
| risk_tolerance | No | Risk tolerance (0-1 scale) | |
| time_to_expiry | Yes | Time to expiry in years | |
| use_market_data | No | Use real-time market data for optimization parameters | |
| underlying_price | No | Current underlying price | |
| market_regime_aware | No | Adjust optimization based on current market volatility regime |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only mentions 'real market data integration' but does not disclose side effects, whether it fetches live data, caching behavior, or failure modes. The one-liner leaves significant gaps in understanding what happens when the tool executes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 13 words, front-loaded with the primary action 'Find'. It contains no filler and every word adds value, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the rich parameter schema, the description is too terse for an 11-parameter optimization tool with no output schema. It fails to explain how parameters interact, what 'optimal strikes and barriers' means in terms of return values, or how market data integration influences the optimization. This is a significant gap for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so all 11 parameters have descriptions in the input schema. The description adds no parameter-specific details beyond what the schema already provides, which is acceptable given the high coverage. Baseline of 3 is appropriate because the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear, specific purpose: find optimal strikes and barriers for structured products. The mention of 'real market data integration' distinguishes this tool from sibling simulation, backtesting, and risk analysis tools, making it unambiguous what activity this tool performs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for optimizing structured product parameters when real market data is needed, but it does not explicitly state when to use this tool over alternatives or provide exclusions. It offers a contextual cue ('real market data integration') but no direct guidance on selection versus sibling tools like run_monte_carlo_simulation or stress_test_scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive backtesting analysis with transaction costs, rebalancing strategies, and performance metrics
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Array of stock symbols for backtesting (e.g., ['AAPL', 'MSFT', 'GOOGL']) | |
| strategy | Yes | Portfolio strategy to backtest | equal_weight |
| initial_cash | No | Initial portfolio value | |
| backtest_period | No | Number of trading days to backtest (default: 252 = 1 year) | |
| use_market_data | No | Use real market data for backtesting | |
| transaction_cost | No | Transaction cost as percentage (e.g., 0.001 for 0.1%) | |
| rebalance_frequency | No | Portfolio rebalancing frequency | monthly |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only lists features. It doesn't mention that it may use real market data, how long execution takes, whether it modifies external state, or what the output format is.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler, and it is front-loaded with 'Comprehensive backtesting analysis.' However, it is under-specified for the tool's complexity, which makes it less appropriately sized than a description that would include key usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters, no output schema, and no annotations, the description is insufficient. It provides no information about return values, data sources, execution characteristics, or how to interpret 'performance metrics,' leaving major gaps for an AI agent deciding to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with all 7 parameters described, so the baseline is 3. The description mentions transaction costs and rebalancing strategies, which map to transaction_cost and rebalance_frequency, but it doesn't add any extra meaning or constraint beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs comprehensive backtesting analysis, citing transaction costs, rebalancing strategies, and performance metrics. While it doesn't explicitly name sibling tools, the combination of these features helps differentiate it from alternatives like run_monte_carlo_simulation or stress_test_scenarios.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus siblings such as run_strategy_comparison or run_walk_forward_test. The description lacks context on prerequisites, data assumptions, or scenarios where this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Monte Carlo robustness testing for portfolio strategies with confidence intervals and parameter sensitivity analysis
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Array of stock symbols for robustness testing | |
| strategy | Yes | Portfolio strategy to test for robustness | mean_variance |
| block_size | No | Block size for bootstrap sampling (days) | |
| num_simulations | No | Number of Monte Carlo simulations | |
| use_market_data | No | Use real market data for robustness testing | |
| confidence_level | No | Confidence level for intervals (e.g., 0.95 for 95%) | |
| parameter_perturbation | No | Parameter perturbation level (0-1 scale) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosure. It mentions outputs (confidence intervals, sensitivity analysis) but is silent on side effects, network/data dependencies, or return behavior. This is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise phrase that effectively communicates the core function without unnecessary words. It is appropriately front-loaded, though it could be slightly more detailed without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters, no output schema, and no annotations, this one-line description is insufficient. It doesn't explain what results are returned, prerequisites, or how to interpret the confidence intervals and sensitivity analysis.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific information beyond the schema, but the schema already documents all seven parameters clearly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: Monte Carlo robustness testing for portfolio strategies, including confidence intervals and parameter sensitivity analysis. This distinguishes it from sibling tools like run_monte_carlo_simulation, though it lacks an explicit verb and doesn't name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. The description only states what the tool does, not when it should be chosen over run_monte_carlo_simulation or stress_test_scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run Monte Carlo simulations for exotic payoffs and risk analysis
| Name | Required | Description | Default |
|---|---|---|---|
| volatility | Yes | Annual volatility (e.g., 0.25 for 25%) | |
| product_type | Yes | Type of exotic product | |
| strike_price | Yes | Strike price | |
| barrier_level | No | Barrier level for barrier options | |
| risk_free_rate | Yes | Risk-free interest rate (e.g., 0.05 for 5%) | |
| time_to_expiry | Yes | Time to expiry in years | |
| num_simulations | No | Number of Monte Carlo simulations | |
| underlying_price | Yes | Initial underlying price |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It only restates the tool's function and does not describe return format, computational intensity, side effects, or whether it is read-only. For a simulation tool, users would benefit from knowing it produces a distribution of outcomes or price paths, but this is absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler or redundant content. It front-loads the main action (Run Monte Carlo simulations) and provides context (exotic payoffs and risk analysis). However, it is so short that it borders on under-specification, but for pure conciseness efficiency, it earns a 4.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (8 parameters, no output schema, no annotations), the description is inadequate. It fails to explain what the simulation returns (e.g., price estimate, confidence intervals, risk metrics), how to interpret outputs, or any caveats. This leaves significant gaps for the agent to correctly use and understand the tool's results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 8 parameters have complete descriptions in the schema (100% coverage), so the baseline is 3. The description adds no additional semantic meaning beyond the schema fields; it merely mentions 'exotic payoffs' which is already captured by the product_type enum. Thus, the schema does the heavy lifting, and the description contributes little.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs Monte Carlo simulations, with a specific focus on exotic payoffs and risk analysis. This aligns with the parameter enum (autocallable, barrier, asian, lookback) and conveys a clear purpose. However, it does not differentiate from the sibling tool run_monte_carlo_robustness_test, which also involves Monte Carlo simulations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as run_monte_carlo_robustness_test or stress_test_scenarios. It does not mention exclusions, prerequisites, or appropriate use cases beyond the generic statement, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Comprehensive comparison of multiple portfolio strategies with backtesting, walk-forward analysis, and Monte Carlo validation
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Array of stock symbols for strategy comparison | |
| strategies | No | Strategies to compare | |
| use_market_data | No | Use real market data for comparison | |
| benchmark_symbol | No | Benchmark for performance comparison | SPY |
| monte_carlo_sims | No | Number of Monte Carlo simulations for robustness testing | |
| comparison_period | No | Number of trading days for comparison analysis | |
| rebalance_frequency | No | Rebalancing frequency for all strategies | monthly |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It does tell the agent that the tool runs backtesting, walk-forward analysis, and Monte Carlo validation, which is useful. However, it does not disclose potentially important traits such as heavy computation time, dependence on market data, network access, or the nature of the returned results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that front-loads the core purpose and key analysis components. Every word earns its place, with no fluff or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a complex tool with 7 parameters and no output schema. The description gives a high-level summary but does not mention what the tool returns (e.g., a report, metrics, plots) or any side effects. For a tool performing potentially time-consuming simulations, the lack of output guidance and resource expectations leaves the agent under-informed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with descriptions, so the baseline is 3. The tool description adds no additional parameter-level meaning beyond what the schema already provides, so it neither helps nor hurts.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a clear verb ('comparison') and resource ('multiple portfolio strategies'), and names the three concrete analysis components (backtesting, walk-forward analysis, Monte Carlo validation). This clearly distinguishes it from sibling tools like run_backtesting_analysis or run_monte_carlo_simulation, which are single-component tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The word 'comprehensive' implies this tool is used when a full multi-method comparison is desired, but there is no explicit guidance on when to choose this instead of the separate sibling tools (e.g., run_backtesting_analysis, run_walk_forward_test, run_monte_carlo_robustness_test). No exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Walk-forward optimization testing to validate strategy robustness over time
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | Array of stock symbols for walk-forward testing | |
| strategy | Yes | Portfolio strategy to test | mean_variance |
| step_size | No | Step size for rolling window in days | |
| use_market_data | No | Use real market data for testing | |
| in_sample_period | No | In-sample optimization period in days | |
| out_of_sample_period | No | Out-of-sample testing period in days |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only says 'testing' which implies a non-mutating operation. It does not state whether the tool is read-only, whether it fetches or modifies data, what it returns, or any side effects. For a tool that performs optimization, the lack of behavioral context is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the key action ('Walk-forward optimization testing') and states the purpose without any filler. It is efficient and scannable, with every word contributing meaning, so it earns a perfect score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite a well-described schema, the tool has no output schema and no annotations, and the description is too minimal to convey the full operational context. It doesn't explain the mechanics of walk-forward testing (e.g., rolling window behavior), data source implications, or what the result looks like. Given the tool's complexity (6 parameters), this short description leaves the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema descriptions cover all 6 parameters with clear definitions (e.g., 'Step size for rolling window in days', 'In-sample optimization period in days'), achieving 100% coverage. The tool description adds no parameter-specific meaning, but it doesn't need to since the schema already provides adequate semantics. This matches the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies a specific function: walk-forward optimization testing with the goal of validating strategy robustness over time. It uses a specific verb ('testing') and resource ('strategy robustness'), and the term 'walk-forward' differentiates it from general backtesting siblings. However, it does not explicitly distinguish itself from related tools like run_backtesting_analysis or run_strategy_comparison, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'to validate strategy robustness over time' implies a use case for time-series validation, but the description offers no explicit guidance on when to choose this tool over siblings or when not to use it. It does not mention alternatives or exclusions, making usage largely implied rather than clearly instructed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Perform stress testing across different market conditions with real market data integration
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | Stock symbol for real market data (e.g., 'AAPL', 'TSLA') | |
| scenarios | No | Custom stress scenarios. If not provided, will use historical market stress scenarios | |
| volatility | No | Base volatility. If not provided and use_market_data=true, will be calculated from historical data | |
| product_type | Yes | Type of structured product | |
| strike_price | Yes | Strike price | |
| barrier_level | No | Barrier level for barrier products | |
| risk_free_rate | No | Risk-free rate. If not provided and use_market_data=true, will fetch current Treasury rate | |
| use_market_data | No | Use real-time market data and calculated volatility for base case | |
| underlying_price | Yes | Current underlying price | |
| include_historical_scenarios | No | Include historical market crisis scenarios (2008 Financial Crisis, 2020 COVID, Dot-com Bubble) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only mentions 'real market data integration' but does not disclose side effects, return format, potential network dependencies, latency, or that it does not modify data. The schema hints at some behaviors (e.g., use_market_data), but the description adds no extra behavioral context beyond a basic action statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it is under-specified for a tool with 10 parameters. It repeats the essence of the tool's name ('stress_test_scenarios' becomes 'stress testing') without adding efficient, unique value. It lacks any structure or breakdown, though it is not verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, no output schema), a one-sentence description is inadequate. It doesn't explain what results are returned, how to interpret stress test outcomes, or the role of default historical scenarios. The schema provides parameter details, but the description fails to provide a complete picture of the tool's behavior and context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all 10 parameters have individual descriptions. The tool description adds no additional parameter insights, examples, or interdependencies. Baseline 3 is appropriate because the schema does the heavy lifting and the description doesn't compensate or enhance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Perform stress testing across different market conditions with real market data integration'. It uses a specific verb (perform) and resource (stress testing), and the mention of 'market conditions' and 'real market data' distinguishes it from Monte Carlo simulation or backtesting. However, it doesn't explicitly mention that it's for structured products or financial instruments, which would further differentiate it from similar analysis tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like run_monte_carlo_simulation or run_backtesting_analysis. The description does not state use cases, prerequisites, or exclusions. It only says what it does, not when to employ it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test cache performance with timing comparisons across multiple API calls
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | Stock symbol to use for cache testing (default: AAPL) | AAPL |
| test_cycles | No | Number test cycles to run (default: 3) | |
| clear_cache_first | No | Clear cache before testing to measure from cold start (default: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing side effects. It does not mention that the tool makes actual API calls, may clear the cache (via clear_cache_first), or that it could impact cache state. The behavioral details are vague, leaving the agent unaware of potential consequences.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that communicates the core purpose without extraneous words. It is well-structured and front-loaded, but lacks any additional sections or examples that could enhance clarity for a tool with a few optional parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description should explain what the tool returns, how timing comparisons are presented, and what side effects occur. The current description is minimal and leaves critical information missing, such as whether results are printed or returned as data, and whether the cache is warmed or cleared.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description itself adds no parameter-specific information, but the schema already documents each parameter well (e.g., 'Clear cache before testing to measure from cold start'). The description does not degrade or improve the semantic clarity beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies a verb ('Test') and a resource ('cache performance'), and adds context about timing comparisons across API calls. It distinguishes from sibling 'cache_status' by focusing on performance testing rather than status inspection, though it doesn't explicitly name the alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'cache_status' or other analysis tools. There is no mention of prerequisites, intended scenarios, or situations to avoid. The usage is only implied through the generic description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0analyze_advanced_riskanalyze_risk_attributionanalyze_stockbuild_portfoliocache_statuscompare_risk_parity_methodscreate_black_litterman_viewsgenerate_payoff_diagramoptimize_black_littermanoptimize_risk_parityoptimize_structurerun_backtesting_analysisrun_monte_carlo_robustness_testrun_monte_carlo_simulationrun_strategy_comparisonrun_walk_forward_teststress_test_scenariostest_cacheScored 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.
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