lookup_appeal_statistics_by_category

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds key behavioral details: never returns individual case details or arguments, and that it is reference only (historical signal does not predict future outcomes). No contradiction with 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 a single paragraph but efficiently conveys purpose, constraints, use cases, and limitations. It is front-loaded with key information and is not overly verbose. Could be structured with bullet points but remains concise.

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 simple parameter set (2 enums, 1 required), rich annotations, and no output schema, the description covers all necessary context: what the tool does, when to use it, limitations, and source curation. An agent can fully understand its role and constraints.

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 baseline is 3. The schema already provides detailed descriptions for both parameters, including enum meanings. The description adds minimal additional parameter-specific semantics beyond what is in the schema.

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 it aggregates dispute-resolution statistics for Taiwan NHI claim disputes by category and optionally review stage. It specifies what it returns (category counts, rough win-rate signals) and what it does not (individual case details). It also distinguishes from the sibling tool 'count_appeal_precedents_for_rejection_code' by stating 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 explicitly provides use cases ('Use when an agent is helping a clinician understand the general dispute landscape') and counter-indications ('Don't use for code-specific signals'), naming the alternative tool. It also includes a caution about reference only and not predicting future outcomes.

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