get_suggestions

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, covering the safety profile. The description adds the 'lightweight' qualifier, which hints at performance characteristics but does not disclose other behavioral traits like rate limits, pagination, or response format. Since annotations are present, the burden is lower, and the description provides minimal added value beyond them.

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 two sentences, each earning its place. The primary purpose is front-loaded, followed by usage guidance that references a sibling tool. There is no repetition or extraneous information, making it highly concise and well-structured.

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 simplicity (2 parameters, one required), the presence of an output schema, and annotations covering safety, the description is largely complete. It explains the tool's function and when to use it over get_related_queries. A minor gap is that it does not explicitly state that the output is a list of suggestions, but the output schema and tool name make this evident. Overall, an agent has sufficient context to call it correctly.

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 description coverage is 100%, so the schema fully documents both parameters (keyword and language). The description mentions 'seed keyword' but that is essentially the same as the keyword parameter, adding no new syntax, formatting, or semantic details beyond the schema. Thus, the description does not significantly enhance parameter understanding.

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 states a specific action ('Return Google Trends autocomplete suggestions') and identifies the resource ('seed keyword'). It also distinguishes itself from the sibling tool get_related_queries by referencing its purpose and scope, making it clear what this tool does and what it does not.

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 explicitly provides when-to-use ('lightweight autocomplete or entity candidates') and when-to-use-alternative ('use get_related_queries when you need top or rising demand signals'). This directly routes the agent to the correct tool based on the need, with a named alternative.

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