suggest

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

Annotations already indicate read-only, idempotent behavior. The description adds meaningful behavioral detail beyond that: results are ranked, position 0 is the most popular completion, and only one query from one engine is handled at a time. 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 compact and front-loaded: the core purpose appears first, followed only by essential parameter semantics and ranking behavior. Every sentence adds value with no redundancy.

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 read-only suggestion tool with an output schema and annotations, the description covers the key invocation decisions: source choice, language/country semantics, and result ordering. It could be more explicit about keyword semantics, but nothing critical is missing for correct use.

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?

Despite 0% schema description coverage, the description documents the allowed source enum values and explains hl and gl with examples. The required keyword parameter is only implied as the 'query', which is a minor gap given the tool name and first sentence.

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 clear verb-resource behavior: it returns autocomplete suggestions for one query from one engine, in rank order. This distinguishes it from sibling tools like expand_alphabet and trends_* by scope and output type.

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 when to use the tool: for a single-query, single-engine autocomplete lookup. However, it does not explicitly name alternative tools such as expand_alphabet or expand_questions, nor does it give when-not-to-use guidance, so some selection reasoning is left to inference.

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