list_autocomplete_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 of behavioral disclosure. It only states that it 'lists autocomplete predictions depending on the keyword,' which implies a read-only operation but lacks details on rate limits, authentication needs, response format, or potential side effects. For a tool with no annotations, this is insufficient to inform safe and effective use.

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, efficient sentence that directly states the tool's function. It is front-loaded with the core action and resource, with no wasted words. However, it could be more structured by including context or usage hints, but it remains appropriately concise for its purpose.

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 no annotations, no output schema, and a tool that likely returns dynamic data (autocomplete predictions), the description is incomplete. It does not cover behavioral aspects like response format, error handling, or integration with sibling tools. For a tool with moderate complexity and lack of structured data, more context is needed to ensure effective 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?

Schema description coverage is 100%, so the schema already documents all parameters (keyword, lang, region) with descriptions. The description adds no additional meaning beyond implying the keyword drives predictions, but it does not explain how parameters interact or affect results. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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 the tool 'lists autocomplete predictions depending on the keyword,' which provides a vague purpose. It specifies the verb 'lists' and resource 'autocomplete predictions' but lacks specificity about what domain or context these predictions are for (e.g., search suggestions, content recommendations). It does not distinguish from siblings like 'search_for_videos_movies' or 'list_items_with_next_token,' which could involve similar listing functions.

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 offers no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or specific contexts (e.g., for search suggestions vs. content discovery). Given siblings like 'search_for_videos_movies' and 'list_items_with_next_token,' there is no indication of when this autocomplete tool is preferred, leaving usage unclear.

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