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Cancel a job

job_cancel

Cancel a running job while preserving partial timeline and footage. Job settles asynchronously; poll job_status for the final state.

Instructions

Best-effort cancel (checked between actions/scenes/stages). A mid-record cancel salvages the partial timeline + footage. The job settles asynchronously — poll job_status for the final state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYes
errorNo
jobIdYes
stageYes
resultNo
statusYes
demoDirYes
endedAtYes
logFileYes
logTailYes
startedAtYes
scenesDoneYes
scenesTotalYes
currentSceneYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.9.0

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so well: it discloses best-effort semantics, mid-record salvage behavior, and asynchronous settlement. Telling the agent to poll job_status for the final state is exactly the kind of non-obvious behavioral guidance needed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, each carrying distinct value: cancel semantics, mid-record behavior, and async resolution with a pointer to job_status. It is front-loaded with the core purpose and has no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the important behavioral context for a simple one-parameter cancel tool: best-effort, timing, salvage behavior, and how to observe final state. The only notable omission is guidance on where jobId comes from, but the output schema exists and the parameter set is minimal.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the parameter, but it never mentions jobId or how to obtain it. The single parameter's name is self-explanatory, but no additional meaning is added beyond the schema's 'jobId: string'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Best-effort cancel' – a specific verb and resource – and adds concrete behavioral detail about when cancellation is checked. It clearly differentiates from siblings by positioning job_cancel as the stopping operation and explicitly pointing to job_status for final state.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context: cancellation is best-effort, checked between actions/scenes/stages, and the job settles asynchronously, so the agent should poll job_status afterward. It doesn't explicitly state when not to use it or compare with alternatives beyond job_status, but the intended workflow is clear.

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