query_video_result

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

No annotations are provided, so the description must carry the full burden. It does not disclose what happens if the task is still processing, errors occur, or the shape of the response. For a query tool, this behavioral information is critical.

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 a single, concise sentence with no unnecessary words. It efficiently captures the core purpose.

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 asynchronous nature and lack of output schema, the description is insufficient. It does not explain return values, possible states (pending/completed/failed), or how to interpret results, leaving the agent with significant ambiguity.

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 coverage is 100% for the single parameter task_id, and the parameter description is adequate. However, the tool description adds no additional semantic value beyond what the schema provides, so baseline of 3 is appropriate.

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 the verb 'query' and resource 'result of an asynchronous video generation task', which clearly indicates the tool's function. It distinguishes from siblings like generate_video (creation) and wait_for_video (polling), but could be more specific about what data the result contains.

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 siblings like wait_for_video. The agent is not told whether to use this after a certain time, or if it is a one-time check. Missing prerequisites or context about task completion state.

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