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CineFlow Studio

CineFlow Studio

by MilayaMariyaAi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

cineflow_generate_dependency_script

Generate an all-in-one bash/bat script that clones all custom nodes and downloads model weights with resume support, enabling one-click dependency setup.

Instructions

Generate an all-in-one bash/bat script (install_cineflow_models.sh) that clones all custom nodes and downloads all weights with resume support in 1 click.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "cineflow_generate_dependency_script"
  }
}

Input Schema

NameRequiredDescriptionDefault
mirrorNoDownload mirror to use in the script.
profileNoVRAM hardware profile to download weights for (default: auto-detected).
comfy_dirNoTarget ComfyUI directory.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the burden and does disclose real behavior beyond the name: the output is a script file with a specific filename, it clones all custom nodes and downloads all weights, and downloads resume on failure. It does not say whether the script is merely written or also executed, where it is written, whether existing installs are overwritten, or side effects on disk.

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?

A single front-loaded sentence that names the artifact first and then its effects; there is no filler or redundant restatement.

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?

For a three-optional-parameter generator with no output schema and no annotations, the description covers what is produced and its main behaviors (node cloning, weight download, resume). It leaves gaps an agent would care about: whether the script is executed, where the file lands, and what is returned.

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 100%, so each of the three parameters (mirror enum, profile, comfy_dir) is already documented in the schema, and the description adds no further meaning about them. Baseline 3 applies since the schema does the heavy lifting.

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?

States a specific verb (generate) and a concrete resource (an all-in-one bash/bat script named install_cineflow_models.sh), then describes exactly what the script does: clone custom nodes and download weights. It is clear what the tool produces, but it does not distinguish itself from plausible siblings such as cineflow_cloud_script_plan or cineflow_generate_launcher.

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

Usage Guidelines3/5

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

Usage is only implied: 'all-in-one' and 'in 1 click' suggest it is the one-shot path for setting up a machine, which hints at when to pick it. There is no explicit when-to-use vs. when-not, no prerequisites, and no pointer to alternatives for partial installs (e.g. a single model or node).

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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