ctr_vs_benchmark

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

With no annotations, the description carries the full burden and delivers robust behavioral context: it warns that position is an impression-weighted average and not a rank-tracker rank, that deep positions rest on sparse data, that absolute positions are directional, and that results must be presented via interactive artifacts. These are meaningful traits beyond simple 'compare and flag'.

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 longer than average but intentionally structured with bolded IMPORTANT, PRESENTATION, and POSITION sections. It is front-loaded with the core purpose, and each section earns its place by giving critical execution guidance. Slight redundancy exists between the 'do not speculate' and 'say so clearly rather than guessing' instructions, but overall it is well organised and not bloated.

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 tool with two optional parameters and no output schema, the description covers interpretation caveats, data limitations, and required presentation format, which is unusually complete. It does not enumerate the exact fields in the returned data, but the 'flags pages' and 'report exact numbers' phrasing makes the expected output reasonably inferable.

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%, providing baseline 3. The description adds relevant meaning beyond the schema by explaining the impression-weighted nature of position and the sparsity of deep-position data, which directly informs the purpose of the min_impressions parameter. This elevates it above the baseline.

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 function: 'Compare your actual CTR per page against industry benchmarks by position' and 'Flags pages significantly underperforming for their ranking position.' This is a specific verb+resource and is distinct in focus from siblings like ctr_opportunities, though it does not explicitly name or contrast alternatives.

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 implies the use case clearly: when you need to compare CTR to industry benchmarks by position and identify underperformers. It also provides explicit instructions on how to handle results ('Base your analysis ONLY on the data returned', 'Do not speculate about causes') and presentation expectations. However, it does not discuss when to choose this tool over sibling tools.

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