clinical_trials_field_values

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

Annotations already cover readOnly, idempotent, non-destructive, and open-world behavior. The description adds useful behavioral context by specifying the output type (top values, counts, distributions) and providing examples of concrete outputs, all consistent with annotations. No contradiction.

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 focused sentences with the main purpose front-loadedaint. The examples are concrete and the only minor fluff is the promotional phrase 'Powerful analytics tool', which does not detract significantly. Overall, every sentence 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 simple read-only tool with two well-documented parameters and no output schema, the description is largely complete. The examples illustrate typical output patterns (counts by phase, top conditions). It does not explicitly describe the exact JSON response structure, but the provided context is sufficient for correct invocation in most cases.

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?

The schema already documents both parameters fully. The description goes beyond the schema by giving illustrative examples for the 'fields' parameter (e.g., 'fields='Phase' shows trial count by phase'), which helps an agent understand the expected output shape. It does not mention the optional 'types' filter, but that is already well described in 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 states a specific action ('Get value statistics') on a clear resource ('study data fields'), with concrete examples like 'fields='Phase' shows trial count by phase'. It clearly distinguishes this from search/detail tools, though it does not explicitly differentiate from closely named siblings like clinical_trials_field_sizes.

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 when to use the tool: when you need top values, counts, or distributions for trial fields. However, it provides no explicit alternatives or when-not-to-use guidance, leaving routing decisions to inference rather than direct instruction.

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