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list_jobs

Read-onlyIdempotent

AI generation jobs. type=image: one image job by job_id, or recent image jobs when job_id is omitted (filter by status, limit); poll it after generate_image for the finished urls. type=video: one video job by job_id, or recent video jobs (status includes dismissed, limit); poll it after generate_video for the finished video_url.

Input Schema

NameRequiredDescriptionDefault
typeYesimage or video.
limitNo
job_idNoOne job. Leave out to list recent jobs.
statusNoFilter a job list. dismissed is video only.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds the useful detail that the tool returns finished urls/video_url after polling, but says nothing about pagination, ordering, or behavior of dismissed jobs beyond what the schema already states. Adequate but not rich.

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

Conciseness4/5

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

Two parallel sentences, one per type, with the job_id/omission behavior front-loaded in each. No filler, though the packing of parameters into the sentences makes it slightly dense to parse.

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?

With no output schema, the description usefully hints at the return payload (urls, video_url) and covers both modes of a 4-parameter tool. The main gap is absence of any note on result ordering or paging, but for a read-only list tool this is close to complete.

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 75%, so most parameters are already documented. The description largely restates schema content (job_id omission lists recent, status/limit filters) and the video-only nature of dismissed is already in the schema. It adds some mode-framing but little genuine semantic detail beyond the structured fields.

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

Purpose4/5

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

The description names a specific resource (AI generation jobs) and a specific verb behavior (list one by job_id or list recent), and splits the domain cleanly into type=image vs type=video. An agent can tell what it does at a glance. It stops short of 5 because the resource is a dense compound sentence rather than a crisp standalone purpose statement.

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?

It explicitly says to poll this after generate_image and generate_video to retrieve the finished urls/video_url, which routes the agent from the sibling generation tools to this one. It gives no explicit 'when-not' or alternative for job retrieval, but the workflow context is concrete rather than implied.

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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