gsc_search_analytics

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

Beyond the readOnlyHint annotation, the description discloses important behavioral details: date range echoing in summary.date_range, data_state 'all' being fresher but unstable, and the critical anonymization caveat that query-dimension rows do not sum to property totals. This last warning prevents a real misuse of the data and adds significant value beyond 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 dense but every sentence contributes: metrics, date handling, dimensions, filters, search_type, data_state, and the aggregation caveat. It is front-loaded with the core purpose and avoids filler or repetition.

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 tool's 8 optional parameters and existing output schema, the description covers all critical usage aspects: date formats, dimension values, filter syntax, search_type options, data freshness semantics, and a non-obvious data-caveat. The only lightly covered parameters are row_limit and site_url, but they are self-evident or covered by sibling tools like gsc_list_sites.

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?

With 0% schema description coverage, the description carries the full explanatory burden and succeeds. It explains date formats, default dimensions, filter syntax with an example, valid search_type values, and the meaning of data_state values. These semantics go far beyond the bare property titles in the schema.

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 opens with a specific verb and resource: 'Query Search Console performance data: clicks, impressions, CTR, position.' It clearly identifies what the tool returns and the metrics involved, making it easy to distinguish from siblings like gsc_list_sites or gsc_inspect_url. The mention of dimensions and search types further sharpens the tool's identity.

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 clearly establishes the context for using this tool: querying Search Console performance data with configurable dates, dimensions, filters, search types, and data states. It does not explicitly name alternatives or state when not to use it, but the intended use is unambiguous given the detailed parameter guidance.

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