get_feedback_scores_analytics

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds Enterprise-gating behavior and 403 error response, which is valuable beyond annotations. However, it does not discuss rate limits, pagination, or other behavioral traits.

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 concise sentences: first states the core functionality, second provides usage guidance and enterprise note. No filler, front-loaded with key information.

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?

While the output schema exists (reducing need to describe return values), the description does not mention the extensive filter parameters available in the schema, which is a gap for a tool with 29 parameters. The description is adequate but could be more complete regarding filtering options.

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 schema already documents all parameters thoroughly. The tool description does not add any additional meaning or context to the parameters, so baseline 3 is appropriate.

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 retrieves 'raw feedback-score distribution time-series data with per-score buckets' and distinguishes itself from get_feedback_weighted_analytics, which handles calibrated scores. The verb 'Get' and resource 'feedback-score distribution' are specific and unambiguous.

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 tells when to use this tool ('understand sentiment mix') and when to use the alternative ('get_feedback_weighted_analytics for calibrated scores'). Also notes Enterprise-gating and 403 error on non-Enterprise plans, providing clear context for usage.

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