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

Annotations declare readOnly, idempotent, and non-destructive, and the description adds useful context: it returns prevalence rates, confidence intervals, data source, and stratification, and notes data through 2023 and the source dataset ID. This goes beyond the hints without contradicting them, though it doesn't detail pagination or rate limits.

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 a single, well-organized paragraph that front-loads the main purpose and then provides details on filters, returns, and source. There is minimal verbiage, and it efficiently conveys both scope and examples. It could be split into clearer sentences, but it remains highly readable.

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 description covers the dataset, topics, filtering options, return fields, data range, record count, source, and auth requirements. Since an output schema exists (not shown here) and annotations cover safety, nothing essential is missing for an agent to invoke the tool correctly.

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?

With 100% schema description coverage, each parameter is already comprehensively documented in the schema. The description paraphrases some filters (e.g., topic ID, state abbreviation) but adds little beyond schema, and the examples in the schema already provide specificity. Thus a 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 explicitly states the tool queries the CDC Chronic Disease Indicators dataset, lists the covered topics, filter dimensions, and return fields. This clearly differentiates it from sibling tools like state_compare, topics, and trend, which have distinct scopes (comparing states, listing topics, trends).

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 clearly conveys when to use this tool by specifying the dataset, filter options, and return values, making it evident it's for querying indicator records. It does not explicitly mention alternatives or when not to use it, but the purpose is so well-defined that an agent can infer usage; a small gap in exclusion guidance prevents a 5.

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