get_keyword_suggestions

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

No annotations are provided, so the description carries the full burden. It mentions 'AI-powered' and output metrics but does not disclose behavioral traits such as response structure, rate limits, or authentication requirements. For a tool with no annotations, more detail is needed.

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 a single sentence that efficiently conveys the purpose and output. No redundant or unnecessary words; every sentence earns its place.

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 simple two-parameter tool with no output schema, the description is fairly complete. It explains the core functionality and key outputs (search volume and difficulty). However, it could benefit from mentioning what happens if no seed is provided or how the count default works.

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% and both parameters (seed, count) have descriptions in the schema. The tool description adds the context of 'local SEO' but does not provide additional semantics beyond what the schema already conveys. Baseline score of 3 is appropriate.

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 it gets AI-powered keyword suggestions for local SEO with search volume and difficulty estimates. It uses a specific verb and resource, and distinguishes itself from sibling tools like 'get_ai_visibility' which focus on visibility rather than keyword suggestions.

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 usage for keyword research in local SEO, but does not explicitly state when to use this tool versus alternatives. It lacks guidance on prerequisites, when not to use, or references to similar tools like 'search_businesses' for broader queries.

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