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

With no annotations provided, the description must carry the behavioral disclosure burden. It reveals that this is a read-only screener and adds notes like 'FREE data' and 'Analytics, not advice,' which are useful. However, it does not disclose behavior around output format, rate limits, error handling, or the meaning of min_coverage/audience, leaving notable behavioral gaps.

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 compact and well-structured, with the main purpose front-loaded and the source/mode options laid out clearly. Minor extra content like 'FREE data' and 'Analytics, not advice' adds little operational value, but it does not significantly bloat the text.

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 6 parameters, 0% schema coverage, and no annotations, the description covers the core choices but omits several meaningful parameters and their interactions. The presence of an output schema helps, but the agent still lacks explanation for audience and min_coverage, and whether source and universe are mutually exclusive. This makes the description only partially complete.

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 adds real meaning to source, mode, and universe, which are the core parameters. But limit, audience, and min_coverage are not explained at all, and those names alone do not fully clarify expected values or behavior, especially audience and min_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 states the tool's job: 'Rank a universe of tickers by the composite score.' It mentions specific resource types and modes, making the purpose understandable. However, it does not explicitly differentiate itself from sibling tools such as score_ticker or trending_tickers, so it lacks the explicit sibling distinction needed for 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 description gives actionable guidance on choosing source ('analysts', 'trending', or explicit universe) and mode ('bullish' vs 'divergence'), which tells the agent when each option is appropriate. It does not explicitly state when to choose this tool over alternative sibling tools, but the source/mode guidance is clear enough context for invocation.

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