Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add MetaBrain-Labs/ComfyUI-MCP-Server-Python --skill comfyui-mcp-server-skillgit clone --depth 1 https://github.com/MetaBrain-Labs/ComfyUI-MCP-Server-PythonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/metabrain-labs/comfyui-mcp-server-python/comfyui-mcp-server-skill)<a href="https://agentmods.dev/skills/metabrain-labs/comfyui-mcp-server-python/comfyui-mcp-server-skill"><img src="https://agentmods.dev/badge/skills/metabrain-labs/comfyui-mcp-server-python/comfyui-mcp-server-skill.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.00945 |
| Opus 5 | $0.00028 | $0.00473 |
| Sonnet 5 | $0.00011 | $0.00189 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
comfyui-mcp-server scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ComfyUI MCP Server
When to Use
Activate this skill whenever the user issues any request involving image, video, or multimedia generation. Your role is the ComfyUI Intelligent Operator — you orchestrate generative AI tasks through the ComfyUI MCP server using a strict sequential tool protocol.
Instructions
Step 1: Initialize Context (Required at the start of every session)
Call the core manual tool to retrieve the latest operational rules, parameter-filling strategies, and error recovery mechanisms.
get_core_manual()
Expected output: A complete operations manual document. Use its contents as the governing ruleset for all subsequent tool calls.
Step 2: Retrieve the Workflow Catalog
Before any generation task, fetch the list of all workflows currently supported by the server.
get_workflows_catalog()
Expected output: A JSON array where each entry contains name (workflow identifier), description, and other metadata fields.
STRICTLY FORBIDDEN to use workflow names from memory or guesswork. You MUST pick an exact match from this catalog.
Step 3: Mount the Workflow — Obtain the Parameter Schema
After matching the target workflow from the catalog, call the mount interface to retrieve the valid parameter key map.
mount_workflow(workflowName: "your_workflow_name")
Expected output: A JSON object describing configurable parameters, each with parameter (key name), default_value, and enum_values (if applicable).
All parameter keys MUST come exclusively from this response. STRICTLY FORBIDDEN to fabricate or guess key names.
Step 4: Submit the Task
Map the user's request to parameter key-value pairs and submit the workflow task.
queue_prompt(
workflowName: "your_workflow_name",
parameters: { "nodeId_inputKey": "user_value", ... },
isAsync: false // false = wait for result; true = return promptId immediately (for batch tasks)
)
Expected output (sync): Task execution snapshot including media file URLs (images, videos, etc.) from output nodes.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 121 lines · 56 tokens per session scan A 55d70576613d
comfyui-mcp-server is a skill published in the GitHub repository MetaBrain-Labs/ComfyUI-MCP-Server-Python (6 stars, last pushed 5mo ago), licensed MIT. It adds 56 tokens to every session and 945 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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