CTR vs Benchmarks

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: benchmarks are aggregated from multiple studies (directional only), and the tool's output should be treated as evidence-based and not over-interpreted. It doesn't describe return format, but the strong guardrails about data usage compensate.

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 well-structured: the core purpose is in the first sentence, followed by the use case and the critical behavioral guardrails. It's slightly longer than ideal but every sentence earns its place, especially the IMPORTANT directive that constrains an AI agent's output.

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 read-only comparison tool with no output schema, the description covers what the tool does, why to use it, and how to interpret results. It lacks explicit return-format documentation, but the 'do not speculate, report exact numbers' directive tells the agent what to expect qualitatively. Given the rich parameter schema and readOnly annotation, this is reasonably complete.

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 83%, so most parameters are already documented in the schema. The description doesn't add parameter-specific details beyond what the schema provides, but it does clarify the overall purpose of the parameters (position-based benchmark comparison). Baseline 3 is appropriate given the high schema coverage.

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 states a specific verb ('Compare'), a specific resource ('actual click-through rates against industry-average benchmarks by position'), and the intended use case ('Identifies pages or queries with CTR below expected levels'). This clearly distinguishes it from the sibling tools like search_analytics (which reports raw metrics) and find_opportunities (which is broader).

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 explicitly frames the comparison against industry benchmarks and notes they are 'directional guidance'. It also gives strong guidance on how to use the data: base analysis only on the returned data, report exact numbers, don't speculate, and say if data is insufficient. This is clear usage context with an implied contrast to other analytics tools.

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