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submit_request

Destructive

Submit an async inference request to a ComfyUI deployment with node overrides, file inputs, webhooks, and optional synchronous completion.

Instructions

Submit an async inference request to a deployment.

Backs ``POST /prod/v1/deployments/{deployment_id}/inference``.

Args:
    deployment_id: Target deployment.
    overrides: Partial graph keyed by node_id, e.g.
        ``{"6": {"inputs": {"text": "a cat"}}}``.
        Use ``get_deployment(include_payload=true)`` to discover
        node IDs and input names.
    workflow_api_json: Advanced — run a different workflow without
        updating the deployment. Omit ``overrides`` in this mode.
    extra_data: E.g. ``{"api_key_comfy_org": "comfyui-..."}`` for
        ComfyUI Core API nodes.
    webhook_url: Push-based updates instead of polling.
    webhook_intermediate_status: Fire webhooks on every status
        change, not just terminal.
    wait_for_completion: If true, poll until done and return the
        result inline.
    timeout_seconds: Max wait when wait_for_completion=true.

File inputs: pass a public HTTPS URL or Base64 data URI directly
in the overrides value, e.g.
``{"189": {"inputs": {"image": "https://example.com/photo.jpg"}}}``
or ``{"189": {"inputs": {"image": "data:image/jpeg;base64,/9j..."}}}``.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
overridesNo
extra_dataNo
webhook_urlNo
deployment_idYes
timeout_secondsNo
workflow_api_jsonNo
wait_for_completionNo
webhook_intermediate_statusNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark the operation as non-read-only and destructive; the description aligns and adds useful behavior: async execution, optional polling, webhook callbacks, and the ability to run a workflow without updating the deployment. No contradiction between description and annotations.

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?

Despite covering eight parameters and multiple modes, the description is tightly structured with a leading purpose line, an endpoint reference, and a compact Args list. Examples earn their place rather than padding.

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?

All parameters, prerequisites, file encoding options, and the wait/webhook modes are covered, so an agent can construct valid calls. The main gap is that the default async return shape is not stated; the description only explains the return when wait_for_completion=true.

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

Parameters5/5

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

Schema coverage is 0%, so the description carries the full burden, and it documents all eight parameters with concrete examples, mode constraints, and file input formats. This far exceeds what the bare schema provides.

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 first sentence names a specific action and resource: 'Submit an async inference request to a deployment,' and the endpoint reference pins down the operation. It does not explicitly call out sibling tools like run_model or get_request_status, so it narrowly misses top marks for differentiation.

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 description gives practical context: use get_deployment to discover node IDs, omit overrides when using workflow_api_json, and choose webhooks instead of polling. It does not explicitly state when to choose this tool over run_model or when to use get_request_status afterward, but the submission flow is clear.

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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