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CineFlow Studio

CineFlow Studio

by MilayaMariyaAi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

cineflow_comfy_workflow_execute

Assemble, parameterize, and queue ComfyUI workflow graphs for shots, saving reproducible .comfy.json and sidecar .cineflow.json files alongside media.

Instructions

Assemble, parameterize, save, and queue a ComfyUI workflow graph (LTX, FLUX, Qwen Vision CV, or Higgs TTS). Saves reproducible .comfy.json and sidecar .cineflow.json alongside shot media.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_id": "<project_id>",
      "scene_id": "<scene_id>",
      "shot_id": "<shot_id>"
    },
    "name": "cineflow_comfy_workflow_execute"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsNoParameter overrides to inject into the graph (prompt, seed, steps, cfg, width, height, input_image, filename_prefix).
shot_idYesTarget shot ID (e.g. 'shot_01').
scene_idYesTarget scene ID (e.g. 'scene_01').
workflowNoOptional custom ComfyUI API prompt graph dictionary.
comfy_urlNoOptional custom ComfyUI base URL (default: http://127.0.0.1:8188).
project_idYesTarget project ID.
workflow_idNoOptional verified workflow ID from catalog (e.g. 'ltx_video_img2vid', 'flux_character_turnaround', 'higgs_voice_clone_tts', 'qwen_vision_frame_inspect', 'ltx_video_img2vid_3090').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full disclosure burden. It does usefully reveal file side effects — it saves a reproducible .comfy.json and a sidecar .cineflow.json alongside shot media — but says nothing about whether queueing is synchronous or returns a job handle, whether existing files are overwritten, what ComfyUI availability is required, or how failures surface.

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?

Two sentences, front-loaded with the action and resource, and the second sentence covers output artifacts. The opening verb list is dense but each verb corresponds to a real capability; nothing is padded.

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

Completeness3/5

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

This is a fairly complex, mutation-heavy tool with seven parameters, nested objects, and no output schema or annotations. The description covers what it produces on disk but omits return values, blocking/queue semantics, and preconditions — enough gaps that an agent could still call it incorrectly.

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 coverage is 100%, so the schema already documents all seven parameters including the params override object and the workflow_id catalog examples. The description reinforces the parameterization concept (overrides injected into the graph, custom graph as an alternative) but adds no syntax, defaults, or precedence rules beyond the schema, so baseline 3 applies.

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 description uses specific verbs (assemble, parameterize, save, queue) against a named resource (ComfyUI workflow graph) and enumerates the supported model families (LTX, FLUX, Qwen Vision CV, Higgs TTS). An agent can distinguish this from a retrieval-style sibling like cineflow_workflow_get, though it never states that contrast explicitly.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance, no prerequisites (e.g. a running ComfyUI server), and no comparison to siblings such as cineflow_workflow_get or cineflow_frames_generate. Usage is only inferable from the action verbs, leaving the agent to guess whether this is the right entry point for a given request.

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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