lookup_appeal_statistics_by_category

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds transparency by stating what the tool does NOT return (individual case details, case numbers, arguments) and that it provides only aggregate statistics, which goes beyond the annotations.

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 concise, with the core purpose first, followed by usage guidelines and a caveat. Every sentence adds necessary information without redundancy, making it easy to quickly understand the tool's function and limitations.

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?

Given the tool's 2 parameters (both well-documented in schema), no output schema, and existing annotations, the description fully covers what the agent needs to know: what it returns, when to use it, when not to, and a caveat about its limitations. No gaps in context.

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 100%, with each parameter already having a detailed description. The description adds value by clarifying that the output includes category counts and win-rate signals per category/stage, giving context to the parameters' role in generating these aggregates.

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 aggregates dispute-resolution statistics for Taiwan NHI claims, broken down by category and optionally review stage, returning category counts and win-rate signals. It explicitly distinguishes itself from the sibling tool count_appeal_precedents_for_rejection_code by specifying it is not for code-specific signals.

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 provides explicit when-to-use ('helping a clinician understand the general dispute landscape') and when-not-to-use ('Don't use for code-specific signals — call count_appeal_precedents_for_rejection_code instead'), along with a concrete example. It also includes a caveat that the data is for reference only and does not predict outcomes.

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