mean_variance_optimize

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 mentions the optimization method ('Mean-Variance Optimization (Max Sharpe)') but lacks details on computational behavior, such as whether it requires historical data, handles constraints, or outputs specific metrics. For a tool with no annotation coverage, this is a significant gap in transparency.

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: 'Calculates optimal portfolio weights using Mean-Variance Optimization (Max Sharpe).' It is front-loaded with the core purpose and uses no unnecessary words, 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?

Given the tool's complexity (portfolio optimization with 2 parameters) and the presence of an output schema (which reduces the need to describe return values), the description is minimally adequate. However, with no annotations and 0% schema coverage, it lacks details on behavior and parameters. The description covers the basic purpose but falls short in providing a complete context for effective use.

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 0%, meaning parameters 'tickers' and 'lookback' are undocumented in the schema. The description adds no information about these parameters, such as what 'tickers' represents (e.g., stock symbols) or how 'lookback' is used (e.g., time period for data). With low coverage and no compensation in the description, this score reflects inadequate parameter semantics.

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: 'Calculates optimal portfolio weights using Mean-Variance Optimization (Max Sharpe).' It specifies the verb ('calculates'), resource ('optimal portfolio weights'), and method ('Mean-Variance Optimization (Max Sharpe)'), which distinguishes it from other portfolio-related tools like 'portfolio_risk' or 'risk_parity'. However, it doesn't explicitly differentiate from all siblings, such as 'monte_carlo_simulation' or 'run_backtest', which might also involve portfolio optimization.

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. It doesn't mention prerequisites, context, or exclusions, nor does it reference sibling tools like 'portfolio_risk' or 'risk_parity' that might be related. Without such guidance, users must infer usage based on the tool name and description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.