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cineflow_download_model

Downloads model weights into the correct ComfyUI folder with resume support, or returns the curl command for manual execution.

Instructions

Download a model weight into the correct ComfyUI folder (checkpoints, unet, loras, tts) with resume support or return the curl command.

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": {
      "model_name": "<model_name>"
    },
    "name": "cineflow_download_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
mirrorNoDownload mirror to use.
comfy_dirNoTarget ComfyUI directory.
model_nameYesModel weight name or filename.
execute_downloadNoIf true, executes the curl download immediately. If false, returns destination and curl command.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations supplied, the description carries the full burden. It does disclose non-obvious behavior: resume support and the dual execute-vs-return-curl modes. It does not state whether an existing file is overwritten, network/auth prerequisites for each mirror, or expected size/duration for what is effectively a write-to-disk operation.

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?

A single front-loaded sentence naming the action, destination, and the two possible outcomes—no filler. The parenthetical folder list is dense but relevant, though it slightly duplicates what the comfy_dir parameter could convey.

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?

No output schema exists, so the description should characterize returns, but it only says it 'returns the curl command' without describing the success response or the destination path returned. For a no-annotation tool that writes files to disk, overwrite and failure behavior remain unaddressed, leaving it adequate but incomplete.

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 all four parameters are already documented in the schema, capping the marginal value here at the baseline. The description only adds loose meaning by listing the ComfyUI subfolder categories that comfy_dir can target and flagging that resume is supported for model_name.

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 and resource ('Download a model weight') plus the destination scope ('correct ComfyUI folder (checkpoints, unet, loras, tts)'), which lets an agent distinguish it from catalog-style siblings like cineflow_models_catalog. It stops short of naming any sibling explicitly, so it earns 4 rather than 5.

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?

The phrase 'or return the curl command' implicitly signals a safe preview/dry-run mode versus an actual download, which is usable guidance, but there is no explicit when-to-use/when-not guidance and no routing to alternatives (e.g., the models catalog for discovery).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.