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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint. The description adds valuable context beyond annotations: it runs entirely locally and requires no API key, plus details the return structure. This complements annotations without contradicting them.

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?

Two sentences, front-loaded with purpose, followed by outputs and a key execution detail. No filler—every sentence adds value.

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?

With a single well-documented parameter, output schema present, and annotations covering safety, the description fully addresses the tool's complexity. It explains inputs, outputs, and execution context, making it complete.

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 covers the single parameter 'responses' with descriptions. The description adds meaning by requiring responses to come from the same prompt template with demographic variants, clarifying the expected input beyond the schema's generic 'variant responses'.

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's function: analyzing LLM responses with demographic variants for bias. It specifies the input type (same prompt template with different variants), the output (bias score, sentiment, similarity, verdict), and differentiates from generic siblings like analyze_responses or compare_responses.

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?

It provides clear context for when to use (responses from same prompt template with demographic variants) and what it returns. However, it does not explicitly mention alternatives or when not to use, which prevents a 5.

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