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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context beyond annotations by stating that no API key is needed and that it runs entirely locally. Nothing contradicts 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 two sentences with no filler. It front-loads the core purpose, then lists key outputs, and ends with a valuable local-execution note. Every sentence earns its place.

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?

The input schema fully documents the single parameter, the output schema covers return structure, and annotations cover safety. The description adds the essential context about same-template demographic variants and local execution, making the tool complete to invoke correctly.

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%, so the baseline is 3. The description adds meaning beyond the schema by specifying that responses must come from the same prompt template and by enumerating variant dimensions (gender, origin, age, tone), which clarifies expected values for variantId more concretely than the schema examples alone.

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 states a specific verb ('Analyse') and a specific resource ('a set of LLM responses generated from the same prompt template but with different demographic variants'). The mention of bias scoring plus sentiment and Jaccard similarity clearly distinguishes this from broader 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?

The description gives a clear usage context: use this when you have multiple LLM responses from the same prompt template with demographic variants. It does not explicitly name alternatives or give exclusion criteria, but the intended scenario is well implied.

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