colab_cancel_job

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

With no annotations, the description carries the full burden of behavioral disclosure. 'Request cancellation' suggests an asynchronous action but does not mention side effects, reversibility, error handling, or whether the operation is idempotent. For a destructive operation, this is insufficient.

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, direct sentence with no unnecessary words. It front-loads the action and target, making it easy to scan.

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?

The tool is simple with one parameter, but the description lacks information about response behavior, asynchronous semantics, or failure conditions. Given no output schema and no annotations, these gaps are notable but not critical for a basic cancel operation.

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 0% and the description does not mention job_id at all. Although job_id is self-explanatory, the description adds no meaning beyond the schema field name. With zero coverage, it should at least clarify the parameter's role.

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 states a clear verb ('cancel') and resource ('job'), and specifies the target state ('running or queued'). This distinguishes it from sibling cancellation tools like colab_stop_execution and colab_stop_training, which target different resources.

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 description implies usage for canceling jobs but does not explicitly contrast with alternative tools (e.g., stop_execution, stop_training) or state when this tool should not be used. It gives context but lacks explicit exclusions.

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