gsc_ngrams

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does so excellently: 'Base your analysis ONLY on the data returned', 'Do not speculate about causes', 'If the data does not contain enough information... say so clearly', plus explicit presentation requirements. This goes far beyond the schema and gives agents clear behavioral boundaries.

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 typical, but each section adds value: purpose, analysis constraints, and presentation instructions are clearly separated with capitalized markers. It is front-loaded with the core purpose and structured effectively, though slightly verbose for a simple extraction tool.

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?

The description comprehensively covers what the tool does, how to interpret results, and how to present them, which is essential given the absence of an output schema and annotations. It gives agents enough context to perform the analysis correctly and deliver the expected dashboard-like output.

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?

The schema description coverage is 100%, so all parameters (days, dataset, min_query_count) are already described in the schema. The description adds no additional parameter-specific semantics, so the baseline score of 3 applies.

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 a specific action: 'Extract the most common meaningful terms across your entire query set, ranked by clicks.' It also differentiates itself with 'A lightweight alternative to keyword clustering', positioning it as a simpler term extraction tool compared to more complex clustering approaches.

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?

It provides usage context by positioning itself as a 'lightweight alternative to keyword clustering', implying when it should be used. However, it does not explicitly exclude or name sibling tools, such as gsc_topic_cluster, so there is no direct 'when not to use' guidance, keeping it just below the highest bar.

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