generate

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

Discloses that the tool creates a prediction and by default waits for the result, returning output URLs. Also explains the wait parameter behavior. Lacks information on rate limits or idempotency, but sufficient for a generation tool with no annotations.

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?

Two sentences with no filler, front-loaded with the primary action. Every sentence adds meaningful information.

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?

Covers the core workflow (generate, wait, output), references sibling tools for discovery. Lacks details on error handling or model-specific inputs, but the schema and sibling reference compensate.

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 has 100% coverage, so baseline is 3. Description adds value by explaining the overall behavior (creation, waiting, output URLs) and referencing list_models for model selection, which aids understanding of the 'model' param.

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 tool generates 'an image or video' with a Magicly model, specifying the verb and resource. It differentiates from siblings by referencing list_models for discovery and implying get_prediction for polling.

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?

Explicitly tells when to use list_models to discover model ids and inputs, providing clear guidance on alternatives. Implicitly advises using get_prediction for polling when wait=false.

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