comfy-draftsman

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get_run_status

Read-onlyIdempotent

Poll ComfyUI runs queued without waiting to check queue position, live sampling progress, outputs, errors, and elapsed time; use full_output to retrieve a node's unclipped text.

Instructions

Polling tool for runs queued with run_workflow(wait=False). For long/paid renders, see run_workflow's long-render pattern. Status of a run queued with run_workflow(wait=False): queue position, live step progress while sampling, and outputs (+ error details, elapsed_s) once finished. full_output=<node_id> returns that node's text unclipped.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`prompt_id`	Yes
`full_output`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.19.0Oct 4, 2026
- addedInput schema / properties / full_output
```
Added value: +{
+  "default": "",
+  "type": "string"
+}
```
Changed2 schema fields changedv0.15.1Aug 23, 2026
- removedInput schema / properties / prompt_id / title
```
Removed value: -"Prompt Id"
```
- removedInput schema / title
```
Removed value: -"get_run_statusArguments"
```
First observedv0.2.0Jul 7, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already establish readOnly/idempotent/openWorld, but the description adds genuine behavioral detail beyond them: what the response contains at each stage (queue position, live step progress while sampling, outputs plus error details and elapsed_s once finished). That is useful state-machine context for a polling tool.

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?

Front-loads the essential routing information (queued with wait=False) in the first sentence, then details the return shape. Mildly dense but every sentence carries information; 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?

An output schema exists so return values needn't be detailed, yet the description usefully summarizes lifecycle stages anyway. For a 2-param polling tool with annotations covering safety, the only real gap is the unexplained required `prompt_id`.

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 description coverage is 0%, so the description must carry parameter meaning. It explains `full_output=<node_id>` returns that node's text unclipped, which is real added value, but `prompt_id` — the required parameter — is never explained or tied to the run queue.

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?

States a specific verb and resource (poll status of a run) and ties it to a precise scope: runs queued with `run_workflow(wait=False)`. This scoping distinguishes it from siblings like get_node_info, get_instance_info, and view_output without opening any schema.

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?

Explicitly says this is for runs queued with `run_workflow(wait=False)` and points to `run_workflow`'s long-render pattern for long/paid renders, giving real when-to-use context. It stops short of explicit when-not/exclusion rules and the alternative reference is left as a pointer rather than spelled out.

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