quant_screen_universe

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by detailing the filtering criteria (under $5 or $50M volume), scoring formula, and output structure, which goes beyond basic safety hints.

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 with two well-organized paragraphs: first explaining purpose and algorithm, second detailing parameters and return. No unnecessary words. Front-loaded with the main action.

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 (one complex parameter, annotations present, output schema exists), the description covers the algorithm, filters, scoring, and output format. It lacks edge cases (e.g., empty results) but is sufficient for selection and invocation.

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?

Input schema has 0% coverage for the top-level 'params' parameter, so the description must compensate. It lists the inner fields (tickers, top_n, response_format) and explains defaults, but the schema itself already provides these details. The added value is moderate.

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 verb 'Rank tickers' and specifies the resource (tickers) with a detailed methodology (intraday tradeability, hard-filters, scoring formula, top N). It distinguishes itself from sibling tools like quant_analyze_universe by focusing on screening with specific metrics.

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 does not provide explicit guidance on when to use this tool versus alternatives. No comparisons to siblings (e.g., quant_analyze_setup, quant_analyze_universe) or statements about when not to use it.

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