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

Annotations cover only safety (readOnly/openWorld), and the description adds real behavioral depth beyond them: the data-source fallback (ClickHouse warehouse else live Search Console API), the heuristic limitation ('judges how a query LOOKS, not what the searcher meant'), the single-row caveat, and the ~2-3 day settling lag.

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 purpose is front-loaded in the first clause, followed by the taxonomy, then returns, then caveats and defaults. It is long, but every sentence carries information; the bucket parenthetical examples slightly pad the length.

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?

For a 6-parameter analytical tool with an output schema, an agent has everything needed: what it computes, the allowed buckets, the date defaults and lag, the data-source fallback, and the interpretation caveat. Return values are covered by the output schema and additionally summarized.

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 67% and the description compensates: it enumerates the conceptual bucket groups with example queries, restates the optional date-range default behavior, and explains the aiShaped roll-up. It does not describe siteUrl or searchType, but the remaining schema descriptions are adequate.

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?

States a specific verb (classifies by SHAPE) and resource (a property's queries) with a clearly named goal (surfacing AI Overviews / AI Mode fan-out fingerprints). The bucket taxonomy and the aiShaped roll-up make it unmistakably distinct from siblings like get_query_shape_trend or query_top_queries.

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?

Explains the context of use (the app's Query Analysis report) and gives concrete operating instructions — omit dates for last 28 settled days, data lags 2-3 days. It does not explicitly name a sibling alternative or state when NOT to use it, so it falls short of a 5.

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