replicate_cancel_prediction

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It only states the action without detailing implications (e.g., whether the prediction stops immediately, billing impact, or error states for invalid IDs).

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 single-sentence description is very concise and front-loaded with key information. However, it sacrifices necessary detail, which is a trade-off that slightly reduces effectiveness.

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 presence of sibling tools and the absence of an output schema, the description is insufficient for an agent to correctly invoke the tool. It lacks context on prerequisites, parameter sourcing, and expected behavior.

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?

The input schema has two parameters (api_token, prediction_id) with 0% description coverage in the schema. The description does not explain where to obtain these values or their format, leaving the agent without needed context.

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 action 'Cancel' and the resource 'Replicate prediction', with the qualifier 'running' specifying the scope. It distinguishes from sibling tools like replicate_create_prediction or replicate_get_prediction.

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 is provided on when to use this tool versus alternatives like replicate_list_predictions or replicate_get_prediction. The description does not mention preconditions (e.g., prediction must be running) or postconditions.

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