analyze_reviews

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 disclosure. It does go beyond the schema by describing the nature of the output: grouping complaints by theme, mention counts, recommended improvements. However, it doesn't disclose constraints like how many reviews are analyzed (though max_reviews is a parameter), what happens if the product has few reviews, whether the analysis is real-time or cached, or any rate limits or auth requirements. As an analysis tool it likely has no destructive side effects, but the description doesn't explicitly state that. It provides some behavioral context beyond the schema but misses potential operational details.

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, dense sentence that front-loads the primary purpose and then lists output components. It's efficient and stays under the ideal length, with no filler. The structure is cluttered with a long list of items (complaints, features, quality/packaging/size/usability problems, defects, improvements) but each item adds specificity. Could be slightly restructured for readability but overall concise and informative.

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 3 parameters (one required) and an output schema exists, the description is partially complete. It fully describes the output categories, which is the tool's core value. However, it omits parameter guidance (highest gap) and does not clarify edge cases or limitations. Since the output schema exists, explaining return values is not required, but the lack of parameter semantics and usage context leaves the description incomplete for a correct invocation without further guesswork. A score of 3 indicates adequate but with clear 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 description coverage is 0% and there are 3 parameters with no enum descriptions. The description adds no parameter-level detail. It doesn't explain what product_name should look like (e.g., exact product name as on Amazon, ASIN?), what marketplace values are valid (default amazon.in but not enumerated), or what max_reviews influence (it is intuitive but not stated). The description is entirely about the output, not the inputs. With 0% coverage, the description must compensate for the schema's silent parameters, and it does not.

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 clearly states a specific verb-resource pair ('Analyse customer reviews for a product') and lists concrete output categories (complaints grouped by theme with counts, appreciated features, problems, defects, improvements). This distinguishes it from sibling tools like analyze_review_metrics (focused on metrics) and scrape_amazon_product (raw data retrieval). A slight deduction because it doesn't explicitly name a sibling alternative, but the purpose is specific and detailed.

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 does not explicitly state when to use this tool versus alternatives. It implies a general use case for analyzing reviews but doesn't contrast with analyze_review_metrics, analyze_purchase_signals, or other related tools. No exclusions, prerequisites, or context about when this is the right choice vs. competitors is provided. The tool context suggests it fits a product research workflow, but the description alone leaves selection to the agent's inference.

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