quant_analyze_universe

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, idempotentHint, etc., so the description adds value by detailing method-specific behaviors (e.g., xs_momentum ranks by 12-1 momentum, pairs returns spread statistics). This is sufficient and consistent with 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 well-structured with a clear purpose statement, method examples, and an Args/Returns section. No unnecessary repetition, and it is appropriately sized for the tool's complexity.

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 annotations and output schema existence, the description covers all key aspects: method variants, parameter constraints, return structure, error handling, and linking to sibling tool quant_score_decision. It is complete for an AI agent to 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?

The input schema already provides descriptions for all parameters (100% coverage), so the description's summary of args adds marginal value. However, it enriches the parameter context with behavioral details like 'xs_momentum ranks by 12-1 momentum' and 'pairs returns beta, ADF p-value, z-score', enhancing semantic understanding.

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 it runs universe-based methods (ranking or pairs) across multiple tickers, with specific examples (xs_momentum, pairs_cointegration) that differentiate it from sibling tools like quant_screen_universe or quant_backtest_method.

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 explains ticker count requirements for each method and error conditions, giving context on when to use. However, it does not explicitly exclude alternatives or clarify when to prefer this tool over siblings, missing an opportunity for clearer guidance.

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