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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains what metric is computed and what output facets are included, which implies a read-only reporting operation, but it does not explicitly state return format, caching, side-effect absence, or any access requirements.

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 dense sentence that front-loads the metric name and uses a dash-separated list to enumerate outputs. Every clause adds useful information, with no repetition or filler.

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 no-parameter report tool, the description covers scope, definition of the metric, totals, rate, monthly trend, per-outlet appearances, and its role as a core longitudinal measurement. It does not specify the exact return shape or data structure, but since there is no output schema, this is a minor gap.

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 tool has zero parameters and the input schema is empty, so schema coverage is effectively 100%. The description correctly does not need to explain parameter semantics, and the baseline for a no-parameter tool applies.

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 identifies the Framing Index as a specific longitudinal measurement across the cross-outlet corpus, with enumerated outputs such as totals, rate, monthly trend, and per-outlet appearances. It is distinct from sibling tools like get_claim_ledger or get_coverage_record, though it does not explicitly name any alternative.

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 phrase 'The desk's core longitudinal measurement' implies a use case, but the description gives no explicit when-to-use or when-not-to-use guidance, and does not contrast this tool with any sibling. An agent would have to infer when to select this over other get_* tools.

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