amazon_autosuggest

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 states it returns keyword suggestions, which is a clear behavioral outcome, but does not disclose potential limitations (e.g., network dependence, rate limits, or what happens if the query is already clean). The behavior is simple and non-destructive, so a 3 is appropriate.

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 concise sentences, front-loaded with the primary purpose, followed by a usage hint and output description. No wasted 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?

The tool has an output schema (not shown), so return values are covered by that. The description covers the purpose, when to use, and what the output is used for. It could mention edge cases or failure modes, but for a simple autosuggest tool, the coverage is adequate.

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 0%, so the description must compensate. It explains the 'query' parameter as the messy shopper wording, adding context beyond the schema's basic type. It also clarifies the purpose of the parameter within the tool's workflow, providing meaningful value.

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 ('correct and complete') and resource ('Amazon.in search query'), and mentions it returns keyword suggestions. It implies a role as a preprocessing step, but doesn't explicitly name sibling tools like 'autosuggest' to differentiate them, which would strengthen clarity.

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?

Explicitly states when to use: 'when the shopper's wording is messy (typos like 'lonovo loq')'. It also indicates the output feeds into amazon_search or compare_marketplaces, but doesn't explicitly state when not to use or list alternatives beyond that implied flow.

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