SoraMCP

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

No annotations provided, so description carries full burden. It discloses return format ('Table of all models') and content (limits, features, use cases), but omits operational traits like caching, rate limits, or safety classification that would help an agent understand invocation constraints.

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?

Four sentences with zero waste: purpose (sentence 1), details (sentence 2), usage guidance (sentence 3), return value (sentence 4). Information is front-loaded and appropriately sized for the tool's simplicity.

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 (per context signals) and zero parameters, the description is complete. It appropriately summarizes the return value without duplicating the output schema, and covers all necessary context for a discovery endpoint.

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?

Zero parameters present, triggering baseline score of 4 per rubric. Schema is empty object with 100% coverage trivially satisfied. No parameter semantic elaboration needed or possible.

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 uses specific verb 'List' with clear resource 'Sora models and their capabilities'. It effectively distinguishes from video generation siblings (sora_generate_video, etc.) by focusing on discovery rather than creation.

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?

Provides clear when-to-use guidance: 'Use this to understand which model to choose for your video generation.' Establishes the discovery workflow before generation. Lacks explicit when-not-to-use exclusions, but context is clear.

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