suggest_keywords

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

No annotations are present, so the description carries the burden. It discloses that this is a read-style suggestion API and what it returns. It does not mention errors, limits, or external dependencies, but for this simple tool the core behavior is clear.

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?

Two dense sentences with no wasted words: output shape first, then concrete usage scenarios.

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 one-parameter read tool with an output schema, the description covers purpose, response fields, and usage context. It stops short of documenting edge cases or output size limits, but the core calling context is 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?

The schema gives no parameter descriptions, so the description compensates by clarifying that the keyword is used to retrieve entity suggestions and can disambiguate ambiguous terms. It could add input format details, but the one-parameter surface is simple.

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?

States a concrete operation—returning Google entity suggestions for a keyword—and identifies the response fields (title, type, mid), distinguishing it from trend/time-series siblings.

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?

Gives explicit use cases: disambiguate ambiguous terms or find the canonical topic ID. It doesn't name sibling tools or when not to use the tool, but the guidance is still actionable.

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