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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to reassert safety. It adds valuable behavior details beyond annotations: confidence labels, reason strings, sharpen-suggestions, auto-aligned frequencies, and no inflated n-counts. It does not cover edge cases or failure modes, but the disclosure is strong for a read-only ranking tool.

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 dense but front-loaded: the core purpose appears in the first five words, and every subsequent sentence adds concrete behavioral or selection value. There is no filler or repetition of schema content.

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?

Despite having no output schema, the description specifies the return structure well: a ranking, confidence labels, reason strings, and sharpen-suggestions. The tool is complex enough that a short example or a note about the optional time parameter would round out completeness, but nothing essential blocks correct 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 75%, and the description adds useful semantics by explaining that outcome is the target metric, entity is the subject, and candidates is optional with auto-selection from the ontology. The main gap is the time parameter, which is not described in the schema or the description, though it is optional.

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 opens with a clear user intent — 'Answer what explains this?' — and then specifies the exact mechanism: given an outcome metric and entity, rank other metrics that best explain the outcome. It also differentiates itself from the sibling find_drivers by saying it provides a 'narrative-grade answer.'

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?

Usage guidance is explicit: 'Use this instead of find_drivers when you want a narrative-grade answer.' It also explains the auto-selection behavior when candidates are omitted, and the cross-dataset reach across World Bank, FRED, Eurostat, OECD, WHO and IMF — giving an agent clear conditions for choosing this tool over alternatives.

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