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submit_workflow

Queue an API-format workflow for execution, returning a prompt ID on success or node-specific errors to fix and resubmit until the graph runs without errors.

Instructions

Queue an API-format workflow for execution (loop step 2: RUN).

workflow is the flat API/prompt-format dict: {node_id: {class_type, inputs}}. Do NOT pass litegraph/UI format here.

On success: returns the prompt_id — then call get_result to fetch outputs and get_image to LOOK at them. Running with zero errors means the graph is VALID, not CORRECT — you still have to inspect the pixels. On failure: returns node_errors keyed by node id. That is NOT an iteration — read the error, fix that specific node, and re-submit until it executes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflowYes
client_idNocomfy-mcp

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description fully compensates. Details both success (returns prompt_id) and failure (node_errors) outcomes. Explains that zero errors means graph is VALID but not CORRECT, requiring pixel inspection.

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?

Description is front-loaded with purpose, uses bullet-like structure for failure handling. Every sentence adds value; no filler. Efficiently conveys complex information in a compact form.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Completely covers usage context within a workflow system, referencing necessary post-execution steps and sibling tools. Output schema exists, so return values need not be described. All essential behavioral aspects are addressed.

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 has 0% description coverage; description adds critical meaning for 'workflow' parameter (flat API dict format). 'client_id' is not elaborated but has a default. Description greatly enhances understanding of the primary parameter.

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?

Clearly states the tool queues an API-format workflow for execution (loop step 2: RUN). Specifies the input format as flat API/prompt-format dict, distinguishing it from litegraph/UI format. Action and resource are explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: on success call get_result/get_image, on failure read node_errors and fix specific node. Also warns against passing wrong format. Clearly differentiates from sibling tools.

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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