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ComfyUI MCP Server

by jonpojonpo

comfy-ui-mcp-server MCP server

A server for connnecting to a local comfyUI

Components

Resources

The server implements a simple note storage system with:

  • Custom note:// URI scheme for accessing individual notes

  • Each note resource has a name, description and text/plain mimetype

Prompts

The server provides a single prompt:

  • summarize-notes: Creates summaries of all stored notes

    • Optional "style" argument to control detail level (brief/detailed)

    • Generates prompt combining all current notes with style preference

Tools

The server implements one tool:

  • add-note: Adds a new note to the server

    • Takes "name" and "content" as required string arguments

    • Updates server state and notifies clients of resource changes

Related MCP server: Datetime MCP Server

Configuration

[TODO: Add configuration details specific to your implementation]

Quickstart

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development

Building and Publishing

To prepare the package for distribution:

  1. Sync dependencies and update lockfile:

uv sync
  1. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:

uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

  • Token: --token or UV_PUBLISH_TOKEN

  • Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory E:\Claude\comfy-ui-mcp-server run comfy-ui-mcp-server

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Available Tools

1 tool
generate_imageC

Generate an image using ComfyUI

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesPositive prompt describing what you want in the image
negative_promptNoNegative prompt describing what you don't wantbad hands, bad quality
seedNoSeed for reproducible generation
widthNoImage width in pixels
heightNoImage height in pixels

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'using ComfyUI' but doesn't explain what that entails—e.g., whether it's a local/remote service, latency, rate limits, authentication needs, or output format (e.g., image file, URL). This leaves significant gaps in understanding how the tool behaves beyond basic functionality.

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 a single, efficient sentence with no wasted words: 'Generate an image using ComfyUI'. It's front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly.

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

Completeness2/5

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, no output schema, no annotations), the description is incomplete. It doesn't cover behavioral aspects like how images are returned (e.g., as files, base64), error handling, or usage constraints. With no output schema, the description should ideally hint at return values, but it doesn't, leaving the agent under-informed.

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%, with all parameters well-documented in the input schema (e.g., prompt, negative_prompt, seed, width, height). The description adds no additional semantic context about parameters, such as typical values or constraints, so it relies entirely on the schema. This meets the baseline of 3 for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Generate an image using ComfyUI' states the basic action (generate) and resource (image) with the specific tool (ComfyUI), but it lacks detail about what kind of image generation (e.g., AI-based, from text prompts) or any distinguishing features. With no sibling tools, differentiation isn't needed, but the purpose remains somewhat vague beyond the high-level action.

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?

The description provides no guidance on when to use this tool, such as scenarios for image generation, prerequisites, or alternatives. It simply states the action without context, leaving the agent to infer usage based on the tool name and parameters alone.

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

TDQS

C2.9/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate. The tool name 'generate_image' follows a clear verb_noun pattern.

Tool Count2/5

A single tool is too few for a server named 'ComfyUI MCP Server', which suggests a broader scope related to image generation workflows. This minimal set likely leaves significant functionality uncovered, making it feel thin and incomplete.

Completeness1/5

The server is severely incomplete for its apparent domain of ComfyUI image generation, as it only offers image generation without any supporting operations like listing workflows, managing nodes, or handling outputs. This single tool creates dead ends and will cause agent failures in complex tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

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