clinical_trials_metadata

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

Annotations already declare readOnlyHint=true, so the read-only behavior is covered. The description adds useful context beyond annotations by explaining what information is exposed (field names, types, descriptions) and how the output is meant to be consumed in other clinical trials tools. It does not contradict 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?

Two sentences with no filler. The first sentence establishes scope and output, the second provides concrete usage guidance. Every phrase earns its place.

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?

For a read-only metadata tool with two optional boolean parameters and full schema coverage, the description is sufficient: it explains the tool's purpose, output content, and relation to sibling search/detail tools. A minor gap is not explicitly mentioning the include_* parameters, but the schema covers those.

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 parameters (include_indexed_only, include_historic_only) already have descriptive text in the schema. The description adds no further parameter-specific detail, so baseline 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 uses a specific verb ('Explore') and names a precise resource ('ClinicalTrials.gov study data model') with concrete outputs ('field names, types, and descriptions'). It clearly differentiates this metadata tool from sibling search/detail tools by framing it as data-model discovery.

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 explicit use cases: discovering fields for the `fields` parameter and building AREA[] filter expressions. It does not explicitly mention alternative metadata-related sibling tools (e.g., clinical_trials_field_values, clinical_trials_enums) or state when not to use them, but the primary use context is clear.

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