gsc_query

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

No annotations are provided, so the description carries the full transparency burden. It discloses the GSC 2-day data lag explicitly, explains the UTC window behavior, notes the paid-plan requirement, mentions output shape (rows with keys[] plus clicks/impressions/ctr/position), and states pagination behavior when capped. The only gap is lack of detail on what happens with oversized result sets beyond startRow pagination.

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?

Comprehensive but compact — three sentences packed with high-value information (dimension list, use cases, lag disclosure, pagination, plan requirement, return shape). Every sentence earns its place, nothing redundant with the schema. Front-loaded with the core purpose.

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 an 11-parameter, highly flexible tool with no annotations and no output schema, the description is remarkably complete. It covers the key decision (dimension selection for different analytical goals), the datetime handling (days vs date range, UTC, lag), filtering patterns, pagination, access requirements, and return format. This addresses essentially every practical question an agent would face in selecting and invoking this tool.

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%, so all 11 parameters are described in the schema itself, giving a baseline of 3. The description adds value above that: it explains the mutual exclusivity/priority between days window vs explicit date range, gives a concrete filter example pattern, and demonstrates dimension combinations. It doesn't fully document the interplay between type/enum values and dimensions, but builds meaningfully on 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?

Description clearly states it runs arbitrary GSC Search Analytics queries with specific verb+resource ('raw, flexible tool'). It names all six dimensions, explicitly enumerates use cases (['date'] for trends, ['country']/['device'] for splits, filters for focusing), and distinguishes itself as the raw flexible option versus sibling gsc_overview which is more curated.

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?

Provides explicit when-to-use guidance with concrete dimension recipes (day-by-day trends, splits, query/page focus) and operational details: trailing window ends ~2 days ago due to GSC lag, default rowLimit 100 with pagination note, paid plan requirement (Starter+). Though it doesn't name alternative tools explicitly, it positions itself as the flexible counterpart to overview tools, giving clear context.

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