clinical_trials_field_sizes

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

Annotations already declare readOnlyHint=true, and the description adds transparency by specifying the output includes min/max/top sizes for array fields. It clearly communicates that the tool returns aggregate statistics rather than raw data. No contradiction with annotations; the description complements them with useful behavioral detail.

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 short sentences, each contributing useful information: what it does, what it shows, and why it's useful. There is no fluff, redundancy, or irrelevant detail. It is front-loaded with the primary purpose.

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 moderate complexity and lack of output schema, the description adequately explains the return value (min/max/top sizes) and provides context (database and example fields). It covers the essential aspects for an agent to decide whether to use it. However, it does not mention any potential limitations or default behavior beyond what is in the schema, so there is slight room for improvement.

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 fully documents the 'fields' parameter with format and default behavior ('Omit for all'), so the description adds no additional parameter semantics. The description's examples (Condition, Intervention, Phase) mirror the schema's description, providing no extra value. With 100% schema coverage, the baseline score of 3 is appropriate.

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 gets statistics on list/array field sizes in ClinicalTrials.gov, using specific verbs and resource. It mentions example fields and output format, making the purpose clear. However, it does not explicitly differentiate from the sibling tool 'clinical_trials_size_stats', so it falls short of a perfect score.

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 a general use case ('useful for understanding data distribution') which implies when the tool might be used, but it does not explicitly state when to use this tool over alternatives or provide any exclusions. There is no mention of sibling tools or conditions under which another tool would be preferred.

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