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invokeai_queue

Inspect, control, and harvest InvokeAI queue items: check status, list jobs, cancel batches, and retrieve generated image results.

Instructions

Manage the InvokeAI generation queue and retrieve results.

[RATIONALE] Queue lifecycle (inspect, control, and harvest outputs) is one domain, so all operations share this portmanteau. 'result' is the natural partner of invokeai_generate: it polls an item to completion and returns the output image URLs (or downloads the file locally).

Return Format

{"success": bool, "operation": str, "data": {...}, "message": str}

Examples

invokeai_queue(operation="status") invokeai_queue(operation="list", status_filter="completed", limit=10) invokeai_queue(operation="result", item_id=123, wait_seconds=120, download_image=True) invokeai_queue(operation="cancel_batch", batch_ids=["batch-uuid"])

Notes:

  • result polls every 3s up to wait_seconds; use wait_seconds for synchronous flows.

  • Without wait_seconds, result returns the current item state immediately.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax items to list.
item_idNoQueue item id (required for item_status, result, cancel).
batch_idsNoBatch ids to cancel (cancel_batch).
operationYesQueue operation to perform.
wait_secondsNoFor 'result': poll until the item completes (max seconds), then return outputs.
status_filterNoFilter list by status (pending, in_progress, completed, failed, canceled).
download_imageNoFor 'result': save the output image to the local download dir and return its path.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the transparency burden and does well by disclosing the return format, polling behavior ('polls every 3s up to wait_seconds'), and immediate-return behavior without wait_seconds. It does not detail potential destructive effects of operations like 'clear' or 'cancel', which would warrant a higher score.

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?

The description is organized into clear sections (rationale, return format, examples, notes) and is appropriately sized for a multi-operation tool. Every sentence contributes, with no filler, and the key summary is front-loaded in the first line.

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?

Given the tool has 9 operations and 7 parameters, the description covers many through examples, return format, and polling notes. It could be more complete by explaining each operation explicitly (especially clear, pause, resume), but the schema and examples cover the main use cases, and the output schema exists.

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?

The schema already provides 100% parameter coverage, but the description adds value by giving operation-specific context: wait_seconds is explained with polling semantics, download_image is described as saving locally, and examples show how parameters like status_filter and limit are used. This goes beyond the raw 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?

The description opens with a clear verb and resource: 'Manage the InvokeAI generation queue and retrieve results.' It explicitly distinguishes the 'result' operation as the natural partner of invokeai_generate, and the rationale explains the grouping of queue lifecycle operations, differentiating it from sibling tools.

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 rationale and examples provide clear context for when to use the tool and its operations, especially the 'result' operation and wait_seconds for synchronous flows. However, it does not explicitly name alternative tools for non-queue tasks, only implies that invokeai_generate is the upstream partner.

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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