feedback_metrics_by_metric

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

Annotations indicate readOnly, idempotent, non-destructive. The description adds valuable behavioral context: it explains that certain parameters (productId, hosting, addon) are ignored, and describes the flat response structure without datasets or uniqueTotal. It also enumerates the series names for each metric value.

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 front-loaded with a one-sentence summary, then details output structure, metric values, filter behavior, and a spec link. It is well-organized but slightly wordy; each 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?

Given the absence of an output schema, the description covers the output format thoroughly, including series structure and addon arrays. It also notes ignored filters. However, it does not explicitly state that the data is scoped to the authenticated developer space or mention pagination limits.

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%, but the description adds meaning beyond the schema by explaining that only aggregation/startDate/endDate are used, and that metric values determine the series names (reasonKeys vs feedbackTypes). This helps the agent understand the impact of parameter choices.

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 returns feedback time-series grouped by a metric, with specific details on output structure and metric values. However, it does not explicitly differentiate from sibling tools like evaluations_by_metric or feedback_details, which handle similar data.

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 lacks guidance on when to use this tool versus alternatives. It mentions that only aggregation/startDate/endDate are effective filters, but does not explain when to choose this over other feedback or metrics tools.

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