play_list_anomalies

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

With no annotations present, the description must carry all behavioral details, but it only states the list intent. It fails to disclose response format, authentication prerequisites, rate limits, or whether it is read-only, leaving the agent in the dark about operating effects.

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?

A concise single sentence that fronts the primary verb and outcome, with the '[Anomaly Detection]' label providing scope without bloat. There is no redundancy, and every word contributes.

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 there is no output schema and no annotations, the description is thin on what the agent will actually receive: response shape, time, and how pagination threads through. The schema covers parameters; a few words about return structure (or reference to page_token behavior) would help close the gap.

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?

The input schema covers all 4 parameters with descriptions (100% coverage), so the tool description does not need to repeat them. It adds no parameter-level meaning, but the schema handles that burden, earning the baseline score of 3.

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 uses a specific verb 'list' and identifies a precise resource: production metric anomalies and sudden regression spikes. The mention of Google Play algorithms and the '[Anomaly Detection]' label clearly differentiates this from siblings like play_get_metric_trends or play_get_error_reports.

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 implies usage for retrieving Play-detected anomalies but offers no explicit when-to-use guidance or alternatives. It does not tell the agent when to choose this over analyzers like play_get_metric_trends, nor does it mention any exclusions.

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