clinical_search

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable context: results are external data (not instructions), fresh for 6 hours, and include a URL for full registration via scrape_page. No contradictions with annotations.

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 a single, well-organized paragraph that is dense but clear. It front-loads the primary purpose and follows with filters, return fields, and related tools. While not extremely concise, every sentence adds value.

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 8 parameters, full schema coverage, an output schema, and comprehensive annotations, the description covers all necessary context: data source, usage domain, freshness, relationship to other tools, and return format. No gaps are evident.

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 covers 100% of parameters with descriptions. The description further clarifies usage patterns (e.g., 'Query by free text, condition, intervention, or sponsor') and provides example values ('covid-19', 'remdesivir'), adding semantic depth 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 identifies the tool as searching ClinicalTrials.gov for clinical studies, specifies the fields searchable (free text, condition, intervention, sponsor), and lists return fields (NCT id, title, status, etc.). It distinguishes from siblings like academic_search and verify_citation, earning a top 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?

Explicit usage guidance is provided: for evidence-based medicine and systematic reviews, not medical advice. It lists specific sibling alternatives (academic_search for published literature, verify_citation for cited studies, web_search for health news) and when to use each, leaving no ambiguity.

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