get_feedback_models_analytics

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 value by disclosing the enterprise gating and 403 error behavior, which is not in annotations. No contradictions.

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: first delivers the core purpose, second adds usage guidance and enterprise restriction. No redundancy, every sentence adds value. Front-loaded with the main action.

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 description covers purpose, usage guidance, and enterprise restriction. With an output schema present, return values are not needed. For a complex tool with 29 parameters, the description provides sufficient high-level context. Could mention pagination or ordering, but not essential given schema completeness.

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 the baseline is 3. The description does not add parameter-level details beyond the schema, which already provides comprehensive descriptions for all 29 parameters.

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 gets 'feedback time-series data grouped by model' with per-model counts over time. It uses a specific verb ('Get') and resource, and distinguishes from the sibling tool get_feedback_analytics by stating the latter is for 'overall total'.

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?

Explicitly says to use this tool to 'compare feedback volume and satisfaction across models' and to use get_feedback_analytics for the overall total. Also provides context about the enterprise gating and 403 response for non-Enterprise plans.

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