uber_eats_recommend

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 discloses meaningful behavioral traits: the empty-mood pathway returns mood options, and the craving parameter only applies when mood is 'craving.' It also documents the available enumerated values for mood and social context. It could add what the tool returns (recommendation details, whether it's a read-only operation), but the description adds substantial context beyond what annotations would've provided.

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 reasonably tight with a clear opening line, a behavioral conditional (empty mood), and enumerated options. The Args section is redundant with the schema but adds example values, which is valuable. Slightly repetitive (mood enumerated twice - in the options list and in the Args examples) but not excessive.

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 the tool has an output schema (which handles return-value documentation) and 3 optional parameters, the description is fairly complete. It covers when to use it, valid parameter values, and the conditional behavior around mood='craving'. It could specify what the output structure looks like or whether recommendations include prices/ratings, but with an output schema present, the description does enough.

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%, so the description must compensate fully. It provides helpful examples for each parameter ('tired', 'celebrating', 'adventurous' for mood; 'alone', 'family', 'friends' for social_context; 'sushi', 'burgers' for craving), enumerates valid values, and explains conditional use of craving (only when mood='craving'). This significantly enriches the bare schema.

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 what the tool does: 'Get personalized food recommendations based on mood and social context.' The verb 'get' + resource 'recommendations' + scoping by mood/social context is specific. It distinguishes reasonably from siblings like uber_eats_search (which searches for restaurants) and nearby_restaurants, though it doesn't explicitly name alternatives.

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?

It provides clear context for use, including the empty-mood behavior (returns available mood options) and enumerated valid values for mood and social_context. It explains the craving parameter's role when mood is 'craving.' However, it doesn't explicitly state when NOT to use this tool versus nearby_restaurants or search, so it lacks explicit exclusions.

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