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

With no annotations, the description carries the behavioral disclosure burden. It usefully adds that scores are relative to the scanned set and reveals a per-call cost, but it does not describe the return format, pagination, or behavior of parameters like top_n and max_tickers.

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 three concise sentences, front-loaded with the core action and followed by essential details (signals, relative scoring, cost). Every sentence adds information without redundancy or filler.

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?

The tool has no annotations and no output schema, so the description should cover output behavior, but it does not explain what the returned ranking looks like or the difference between top_n and max_tickers. Core purpose and signal list are present, but parameter semantics and return behavior are incomplete.

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%, so the description must compensate. It only enumerates the signal choices and mentions 'stock universe,' but it does not explain range, top_n, max_tickers, or how they interact. The parameter names and enums provide some self-evident meaning, but the description adds little 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 the tool's purpose with a specific verb ('Rank'), a resource ('stock universe'), and the exact ranking semantics ('rank 1 = highest z-score'). It also lists the supported signals, which distinguishes it from generic screens or score computations.

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 the tool is used when you need a cross-sectional signal-score ranking of a universe, but it does not explicitly state when to prefer it over sibling tools like 'screen' or 'compute_universe_scores'. No exclusionary or alternative guidance is provided.

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