Clinicaltrials Get Field Values

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds that the tool returns 'valid values with study counts per value,' which aligns with the read-only and idempotent nature. No contradictions or missing behavioral notes.

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, no wasted words. Front-loaded with the core action ('Discover valid values') and immediately followed by usage context. 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?

For a simple one-parameter tool with an output schema and thorough annotations, the description is fully adequate. It explains the tool's role in the workflow, the return content, and how to start using it with examples.

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%, so the baseline is 3. The description does not add new semantic meaning about the parameter beyond what the schema already provides (PascalCase field names, examples). It does refer to another tool for finding more field names, which is helpful but not required for parameter understanding.

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's purpose: to discover valid values for ClinicalTrials.gov fields with study counts. It provides concrete examples (OverallStatus, Phase) and explicitly positions it as a preparatory step before building a search, distinguishing it from sibling tools like search_studies or get_study_record.

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 instructs when to use the tool ('before building a search') and references a sibling tool (clinicaltrials_get_field_definitions) for finding more field names. It does not explicitly state when not to use it, but the context is clear enough for an AI agent.

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