SoraMCP

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

With no annotations provided, the description carries the full burden. It successfully discloses the async pattern by mentioning 'Task ID' and 'state' in returns, but omits critical behavioral traits like polling requirements (despite the existence of 'sora_get_task'), rate limits, cost implications, or data retention policies.

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 follows a logical hierarchy: purpose statement → capability elaboration → usage conditions → return values. Each sentence earns its place with no tautology, though the three bullet points under 'Use this when' have slight overlap ('animate' vs 'bring static images to life').

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's complexity (6 parameters, async behavior) and the existence of an output schema (per context signals), the description adequately covers the return contract (Task ID, URLs, state). It appropriately focuses on the image-to-video differentiation rather than repeating full schema documentation.

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%, establishing a baseline of 3. The description adds marginal semantic value by explaining that the AI 'will use the images as visual references,' but does not elaborate on parameter interactions (e.g., duration constraints with specific models) beyond the schema itself.

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 opens with a specific verb-resource combination ('Generate an AI video from reference images') and explicitly identifies the modality as 'Image-to-Video' using 'Sora'. This clearly distinguishes it from sibling text-to-video tools like 'sora_generate_video'.

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 'Use this when:' section provides three concrete scenarios (animating reference images, matching visual styles, bringing static images to life) that help the agent identify appropriate contexts. However, it lacks explicit negative guidance (when NOT to use this vs. text-to-video alternatives).

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