Get Keyword Suggestions

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

The description adds significant behavioral context beyond the annotations: it explains pagination (limit/offset), the effect of with_keyword_data, return fields (total_available, has_more, next_offset), and the 'Omit for a general mix' for suggestions_type. No contradiction with annotations.

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 concisely structured with a main sentence followed by bullet points for arguments and return. Every sentence adds value; no extraneous words. The purpose is front-loaded.

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 tool's moderate complexity (7 params, 2 required), high schema coverage, and presence of annotations and output description, the description is complete. It covers input semantics, output structure, and pagination, leaving no major gaps for an AI agent.

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%, but the description adds crucial semantics beyond the schema: it clarifies 'client-side window size' for limit, 'client-side offset into API results' for offset, the default behavior of with_keyword_data, and what 'Omit' means for suggestions_type. This provides valuable insight for proper invocation.

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 verb ('Retrieve'), resource ('related keyword suggestions'), and optional metrics. It explicitly lists specific use cases (topical research, FAQ mining, trend discovery) that distinguish it from sibling tools focused on search volume and website stats.

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 explicit use cases for the tool (e.g., 'Use for topical research, content-cluster ideation, FAQ mining'). While it does not explicitly state when not to use or name alternatives, the context signals (sibling tools) and the clear use cases give adequate guidance.

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