foodpanda_restaurant_menu

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

With no annotations, the description carries the full burden of behavioral disclosure. It thoroughly describes the return structure: menu grouped into categories, opening hours, price range, cuisines, rating, and item-level details including sold-out flag and pricing (original and discounted). This gives a solid understanding of what to expect. It does not mention potential error cases or rate limits, but for a read-only menu fetch, these are less critical. The description adds significant context beyond the schema.

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, front-loaded with the core purpose, and efficiently details the return contents without redundancy. Every sentence earns its place, and the structure is clear and scannable. It is appropriately sized for the complexity of the tool.

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?

Despite having no output schema, the description comprehensively outlines the expected response: menu categories, restaurant-level metadata (hours, price range, cuisines, rating), and item-level details (title, description, image, sold-out flag, pricing). It covers the key aspects an agent would need to understand the tool's output. For a simple read operation with two well-documented parameters, the description is complete enough for correct invocation and interpretation.

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 both parameters (code and market) are fully documented in the schema itself. The description does not add any additional meaning beyond what the schema provides—it references the restaurant code indirectly but doesn't elaborate on parameter usage. According to the rubric, with high schema coverage, a baseline of 3 is appropriate. The description adds no extra parameter-specific guidance.

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 the tool's purpose: 'Get one foodpanda restaurant's menu with prices.' It specifies the resource (restaurant menu) and the action (get), and details the exact contents of the response, distinguishing it from other foodpanda tools like restaurant details or reviews. The verb and resource are specific, and the scope is unambiguous.

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 provides clear context about when to use this tool: whenever a restaurant's menu is needed. However, it does not explicitly mention alternatives or when not to use it. Given the large sibling list, naming alternatives like foodpanda_restaurant for restaurant details would have been ideal, but the description's clarity about its purpose makes the usage context evident. This is a clear context with no exclusions, so a 4 is appropriate.

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