clinical_trials_field_values

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

Annotations already declare readOnlyHint=true, so the description's main behavioral addition is explaining the output (top values, counts, distributions). However, it does not disclose limitations (e.g., maximum number of values returned, handling of large datasets) or whether results are sorted. The examples hint at output form but leave ambiguity.

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 concise (4 sentences) with the primary purpose in the first sentence. It includes a promotional second sentence and relevant examples. Could be slightly more structured by integrating examples earlier, but overall efficient.

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 absence of an output schema, the description partially explains returns ('top values, counts, and distributions') but lacks specifics on the format, sorting, or size limits. The optional 'types' parameter is ignored. While adequate for basic use, it leaves gaps for precise agent invocation.

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 coverage is 100%, setting baseline at 3. The description adds value by providing concrete examples for the 'fields' parameter (e.g., 'Phase', 'Condition') and explaining its pipe-separated format. However, the optional 'types' parameter is not mentioned in the description, missing an opportunity to guide filtering.

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 retrieves value statistics (top values, counts, distributions) for clinical trial data fields, with concrete examples. It distinguishes itself from sibling tools like 'clinical_trials_search' by focusing on field-level aggregations rather than individual trials, but does not explicitly name alternatives.

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 implies usage when needing field-level distributions (e.g., top conditions, phase counts) but provides no explicit guidance on when to use this tool versus similar siblings like 'clinical_trials_enums' or 'clinical_trials_stats'. No when-not-to-use or prerequisite information.

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