Search clinical trials (ClinicalTrials.gov)

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds valuable execution context: it runs the Apify Actor tacps126/clinical-trials on the user's account, costs about $0.003 per study, and caps spend per call at $0.5 — details an agent cannot infer from annotations or schema.

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?

Three sentences are front-loaded with what the tool returns, followed by use cases and cost transparency. The long first sentence packs many fields but every phrase earns its place; no filler or redundancy is present.

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 is complex (11 params, nested extra_input, output schema) but the description covers the key dimensions: data source, filters, returned fields, use cases, execution environment, and cost. With an output schema present, it needn't explain return values in detail, though it could briefly mention pagination or result shape to be fully 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 100%, so the schema already documents every parameter with examples and enum values. The description only restates the filterable dimensions (condition, sponsor, status, phase) without adding syntax or format guidance beyond the schema, which is the expected baseline when the schema does the heavy lifting.

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 names a specific resource (ClinicalTrials.gov trials) and enumerates the filters and returned fields, so an agent knows exactly what data it retrieves. It does not explicitly contrast with sibling tools like search_research or search_fda_device_clearances, so it stops short of the sibling-differentiation bar 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?

It provides concrete use cases — trial landscapes, site and patient recruitment, competitive intelligence, and new-study alerts — which clearly signal when to reach for this tool. It gives no when-not guidance or named alternatives, but the context is clear enough for most selections.

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