clinical_trials_metadata

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds value by specifying that the tool returns schema-level information (field names, types, descriptions) rather than study records or statistics, giving the agent a clear expectation of the response nature.

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?

Two sentences with no filler. The first sentence states what the tool reveals, and the second states exactly when to use it. The front-loaded purpose makes it easy for an agent to quickly judge relevance.

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 low-complexity: zero required parameters, two self-describing optional booleans, and no output schema. The description explains what the tool returns (field names, types, descriptions) and why an agent would call it. Nothing needed for correct selection or invocation is missing.

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%: both boolean parameters are already documented with descriptive names and default values. The description adds no parameter-specific detail, but with full schema coverage that is acceptable; this is the baseline case where the schema carries the semantic weight.

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 verb ('Explore') and resource ('ClinicalTrials.gov study data model') and states the concrete outputs: field names, types, and descriptions. It distinguishes itself from data-returning siblings like clinical_trials_search and clinical_trials_detail by explicitly tying it to 'fields' parameter discovery and AREA[] filter syntax.

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 clear usage context: use it to discover fields for search/detail tools or to construct AREA[] filter expressions. It does not explicitly state when not to use it or name alternatives like clinical_trials_field_values or clinical_trials_enums, but the provided use cases are specific enough for an agent to route itself correctly.

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