Cancel a submitted job by ID to stop running Kaggle or Colab kernels, including kernels Kaggle's CLI cannot cancel; mock mode uses test fixtures, not a live account.
Cancel a job. Mock data are TEST FIXTURES, not a live account.
Kaggle's official CLI cannot cancel a running kernel.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full behavioral burden. It implies a destructive cancellation but does not state whether cancellation is irreversible, what job states are cancellable, permission requirements, or side effects; the mock-data and CLI notes add some context but not enough.
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 short and front-loads the core action. The following sentences add context without excess, though they are not tightly organized around invocation guidance.
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?
For a cancellation mutation with no annotations, the description should clarify safety, reversibility, and parameter semantics beyond what the minimal schema provides. It mentions mock fixtures and a CLI limitation but leaves important invocation details incomplete.
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 schema has one required parameter, job_id, with 0% description coverage, and the description does not explain its format or meaning. The phrase 'cancel a job' only weakly implies that job_id identifies the job.
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 specific verb and resource: cancel a job. It is clear what the tool does, but it does not distinguish itself from the sibling l2kc_cancel_job, so sibling differentiation is missing.
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?
It does not explain when to use this tool instead of l2kc_cancel_job, nor does it state prerequisites or conditions for cancellation. The mock-data and Kaggle CLI notes provide context but not actionable usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.