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cancel-prompt-optimization-job

Cancel a running prompt optimization job by specifying its job ID. This stops the process immediately.

Instructions

Cancel a running prompt optimization job

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesPrompt optimization job ID to cancel

Implementation Reference

  • Handler function that cancels a prompt optimization job by POSTing to the MLflow API endpoint with the job ID.
    export async function cancelPromptOptimizationJob(params: z.infer<typeof cancelPromptOptimizationJobSchema>) {
      assertWriteAllowed();
      return mlflowClient.post(`/api/3.0/mlflow/prompt-optimization/jobs/${encodeURIComponent(params.jobId)}/cancel`);
    }
  • Input schema for cancel-prompt-optimization-job, requiring a 'jobId' string.
    export const cancelPromptOptimizationJobSchema = z.object({
      jobId: z.string().describe("Prompt optimization job ID to cancel"),
    });
  • src/index.ts:268-268 (registration)
    Registration of the cancel-prompt-optimization-job tool with its schema and handler.
    tool("cancel-prompt-optimization-job", "Cancel a running prompt optimization job", cancelPromptOptimizationJobSchema.shape, wrapToolHandler(cancelPromptOptimizationJob));
  • Imports assertWriteAllowed helper used by the handler to enforce write permissions.
    import { assertWriteAllowed, resolveExperimentId } from "./utils.js";
Behavior2/5

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

No annotations are provided, and the description only says 'Cancel' without explaining the effect (e.g., immediate termination, status change) or any side effects.

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?

Single sentence with no extraneous words. Perfectly concise for a simple action.

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?

For a simple cancel operation with one parameter and no output schema, the description is adequate, though it could explicitly state that the job must be running for cancellation to have effect.

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

Parameters3/5

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

Schema coverage is 100% and the schema already describes 'jobId' as the ID to cancel. The description adds no further meaning beyond the schema.

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?

Description clearly states the action (cancel) and the target (a running prompt optimization job), distinguishing it from create, delete, get, etc. siblings.

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

Usage Guidelines3/5

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

No explicit when-to-use or when-not-to-use guidance, but the action is simple enough that the context of 'running' implicitly tells when it's applicable.

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

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