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run_api_node

Run any ComfyUI node in one call: auto-builds the workflow, inserts needed loaders/save nodes, and works with all 200+ API nodes.

Instructions

Run any ComfyUI node in a single call.

Builds a minimal workflow around the node, auto-inserting loader and save nodes as needed. Works with all 200+ API nodes (Kling, Runway, Luma, Stability, ElevenLabs, Gemini, etc.) and any built-in node.

Workflow: discover nodes with list_api_nodes() or search_nodes(), check inputs with get_node_schema(), then run with this tool.

For IMAGE/VIDEO/AUDIO-typed inputs, pass a filename string (e.g. "photo.png") and a LoadImage/LoadVideo/LoadAudio node is auto-inserted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputsNoJSON string of node inputs. Default: "{}". Primitive values: {"prompt": "a cat", "seed": 42}. File inputs: {"image": "photo.png"} — auto-creates LoadImage. Use get_node_schema(node_class) to see all available inputs with their types, defaults, and valid ranges.{}
timeoutNoMax seconds to wait for completion. Default: 0 (use server default, typically 300s). Set higher for slow API nodes like video generation (e.g. 600 for Veo 3).
node_classYesThe node class name (e.g. "KlingTextToVideoNode", "OpenAIDalle3", "ElevenLabsTextToSpeech"). Use list_api_nodes() or search_nodes() to discover available nodes, and get_node_schema() to see required/optional inputs.
queue_onlyNoIf true, queue the job and return immediately with the prompt_id without waiting for completion. Default: false. Use get_job_status(prompt_id) to check progress later, or wait_for_jobs([prompt_id1, prompt_id2, ...]) to wait for multiple jobs at once.
output_formatNoHow to save the output. Default: "auto". "auto" — detect from node outputs (IMAGE→SaveImage, etc.). "image", "video", "audio" — force a specific save type. "none" — skip save node (for nodes that save internally).auto

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 full burden. It discloses key behaviors: auto-insertion of loader and save nodes, and auto-generation of LoadImage/LoadVideo/LoadAudio for file inputs. It doesn't cover error handling or permissions, but the disclosed behavior is substantial and goes beyond a simple 'run' statement.

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 concise and well-structured: first sentence gives purpose, second provides workflow, third gives a concrete file-input example. Every sentence adds useful information without redundancy or 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?

Given the tool's complexity (5 parameters, generic node execution), the description covers the essential workflow, file input handling, and timing (timeout, queue_only). It doesn't explicitly mention output retrieval, but the presence of an output schema and sibling tools like get_result_images mitigates this. It's complete enough for a generic runner.

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 description coverage is 100%, so baseline is 3. The description adds extra value by explaining how file inputs are handled (pass filename string, auto-insert load node) and by referencing get_node_schema() for input details. This pushes it above baseline.

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 clearly states the tool runs any ComfyUI node in a single call, with the specific action of building a minimal workflow and auto-inserting loader/save nodes. This distinguishes it from specialized sibling tools like text_to_image or sora_video_generate, making its generic-purpose role evident.

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 provides an explicit workflow: discover nodes with list_api_nodes() or search_nodes(), check inputs with get_node_schema(), then run. It clearly implies use for arbitrary/uncovered nodes, though it doesn't explicitly state when to prefer specialized tools or list exclusions. The workflow guidance is strong enough to earn a 4.

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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