fetch_reviews

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

No annotations, but description fully discloses behavior: returns raw review array plus metadata, and implies read-only fetch. Could mention rate limits or auth, but overall transparent.

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?

Concise yet complete, with clear sections for overview, usage context, args, and returns. Every sentence adds value.

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?

Covers all necessary aspects given no annotations or output schema: purpose, parameters, return format, and relationship with sibling tools. No gaps.

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 has 0% description coverage, but the description provides detailed semantics for all 3 parameters, including format, examples, defaults, and valid values.

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?

Clearly states 'Fetch raw Amazon reviews for an ASIN' with specific verb and resource, and distinguishes from siblings by emphasizing no analysis and returning raw data.

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 describes when to use (for raw data or prior to analyze_reviews) and when not to (if analysis is needed elsewhere), with alternatives named.

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