foodpanda_restaurant_reviews

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so well. It discloses that results are a sample, not the full logged-in review corpus, that the count scales with popularity, and that the result may be empty for restaurants with few reviews. This goes far beyond a generic 'get reviews' statement and accurately sets agent expectations.

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 two sentences with no filler; the core action is front-loaded and every clause adds useful nuance (source of the sample, scaling behavior, empty-result possibility). It is detailed without being bloated.

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 description is largely complete given the tool's moderate complexity: it explains the returned fields, the sample nature, scaling, and empty results. It does not cover ordering, error behavior, or what happens when the market is unspecified, but the schema covers the market default and the main edge cases are already disclosed.

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%, with both 'code' and 'market' already documented in the input schema. The description adds no additional parameter-level meaning, such as format details or how values relate to output, so the baseline of 3 is appropriate.

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 specific verb ('Get'), a clear resource ('one foodpanda restaurant's customer reviews'), and the returned fields (author, date, text, rating). It is unambiguous about what the tool does, but it does not explicitly name or differentiate itself from sibling tools like foodpanda_restaurant or foodpanda_restaurant_menu, so it falls just short of full sibling-level distinction.

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 by clarifying it returns the curated storefront review sample rather than the full app corpus. However, it never states explicit when-to-use or when-not-to-use conditions, nor does it mention any alternative tool, leaving the agent to infer the right selection solely from the resource type.

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