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posterly-mcp-server

posterly-mcp-server

by awpthorp

list_jobs

Read-onlyIdempotent

Check AI image or video generation job status and retrieve finished image URLs or video_url after starting a generation, so you can poll jobs and collect results.

Instructions

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 Veo or Grok 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 observedv0.55.1

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, so the safety profile is covered; the description adds material context beyond that, such as the video status set including 'dismissed' and the returned fields (urls, video_url). It doesn't cover ordering or pagination of the list results, which would matter for polling loops.

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?

It is dense and front-loaded, with the type=image branch stated before type=video and the polling purpose repeated per branch. The repetition of 'poll it after generate_*' is mildly redundant but earns its place by tying each type to its generator.

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 correctly names the key return values (finished urls for image, video_url for video), which is what an agent needs after a generate call. It leaves the ordering of 'recent jobs' and whether limit caps the list undefined, a minor gap for a four-parameter read tool.

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

Parameters4/5

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

Schema coverage is 75% and the schema already documents type, job_id, and status. The description adds per-type semantics the schema doesn't: that job_id selects a single job of the given type, that omitting it triggers a list, and that status/limit apply to the list form for each type.

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 the resource ('AI generation jobs') and then spells out the two distinct retrieval modes per type: a single job by job_id versus a recent-jobs list when job_id is omitted. It explicitly names the sibling tools (generate_image, generate_video) it pairs with, so an agent can distinguish it from anything else in the catalog.

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 gives clear when-to-use guidance — poll after generate_image for finished urls, poll after generate_video for video_url — and explains the job_id-omitted case as listing recent jobs. It stops short of stating when NOT to use it or naming an alternative tool, so it falls just under a 5.

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