list_recipes

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

No annotations provided, and description fails to disclose behavioral traits such as output format, pagination, ordering, permissions, or side effects. The description is minimal, leaving the agent without critical usage context.

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?

Single sentence, directly states purpose with no extraneous words. Front-loaded and efficient.

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?

Lacking annotations and output schema, the description does not explain return values, potential errors, or scope limitations. For a simple list operation it is barely adequate but incomplete for safe autonomous use.

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% for the single parameter 'dataset', and its schema description is adequate. The tool description adds no additional meaning beyond the schema, but baseline is 3 due to high coverage.

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 verb 'List' and resource 'recommendation recipes' with context 'in an Interaction Studio dataset'. This distinguishes it from sibling tools like list_surveys or list_campaigns, providing specific purpose.

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?

No guidance on when to use this tool versus alternatives (e.g., get_recipe_usage). Does not mention prerequisites or the optional dataset parameter's condition (IS_DEFAULT_DATASET). Agent receives no context for invocation decisions.

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