get_gsc_insights

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'AI-powered content recommendations' and the types of insights returned, but doesn't cover critical aspects like authentication requirements, rate limits, data freshness, error handling, or whether it's a read-only operation. For a tool that likely involves external API calls and data processing, this is a significant gap.

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 efficiently structured in two sentences that clearly communicate the tool's function and outputs. It's front-loaded with the core purpose and avoids unnecessary verbiage. However, the second sentence could be slightly more concise by combining the listed return types.

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 complexity of SEO insights and AI recommendations, with no annotations and no output schema, the description is incomplete. It doesn't explain the format or structure of returned data, error conditions, or behavioral constraints. The agent would need to guess about the response format and operational characteristics.

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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'AI-powered content recommendations' which relates to the 'include_recommendations' parameter, but this is already covered in the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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's purpose: 'Get Google Search Console performance insights with AI-powered content recommendations.' It specifies the verb ('Get'), resource ('Google Search Console performance insights'), and scope ('with AI-powered content recommendations'). However, it doesn't explicitly differentiate from sibling tools like 'get_page_seo' or 'get_issues', which might also relate to SEO analysis.

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 provides no guidance on when to use this tool versus alternatives. It lists what the tool returns but doesn't mention prerequisites, context for usage, or comparisons to sibling tools like 'get_page_seo' or 'crawl_site'. This leaves the agent without clear direction on appropriate use cases.

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