ComfyUI MCP Server
The ComfyUI MCP Server provides the following capabilities:
Note Management: Create, store, and access notes via a custom
note://URI scheme. Each note has a name, description, andtext/plainmimetype.Note Summarization: Generate summaries of all stored notes with optional style control (
briefordetailed) to adjust detail level.Image Generation: Generate images through the connected ComfyUI instance by specifying parameters such as seed, width, height, positive prompt, and negative prompt.
Integrates with a local ComfyUI instance, providing tools for generating AI images based on text prompts with customizable parameters such as dimensions, seed values, and negative prompts.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ComfyUI MCP Serveradd a note named 'meeting notes' with the content 'discussed project timeline'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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:
Sync dependencies and update lockfile:
uv syncBuild package distributions:
uv buildThis will create source and wheel distributions in the dist/ directory.
Publish to PyPI:
uv publishNote: You'll need to set PyPI credentials via environment variables or command flags:
Token:
--tokenorUV_PUBLISH_TOKENOr username/password:
--username/UV_PUBLISH_USERNAMEand--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-serverUpon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
Available Tools
1 toolgenerate_imageC
Generate an image using ComfyUI
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Positive prompt describing what you want in the image | |
| negative_prompt | No | Negative prompt describing what you don't want | bad hands, bad quality |
| seed | No | Seed for reproducible generation | |
| width | No | Image width in pixels | |
| height | No | Image height in pixels |
TDQS
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.
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.
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.
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.
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.
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
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.
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.
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.
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
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Markdown-based note-taking with a hosted MCP server. Your notes serve you and your AI.
Google Keep-style notes app with an MCP server for AI agents to read/write notes.
Cross-session, cross-device memory for your agent: remember and recall notes. No key to start.
Persistent memory layer for AI tools. Save and recall notes across Claude and other MCP clients.
Related MCP Servers
- FlicenseCqualityDmaintenanceThis server provides a note storage system with a custom URI scheme, allowing users to add and summarize notes, with adjustable summary detail levels.1
- FlicenseCqualityDmaintenanceThis server enables users to store, manage, and summarize notes using a custom URI scheme, with functionality to add new notes and generate summaries with varying levels of detail.3
- FlicenseBqualityDmaintenanceA server for managing and summarizing notes using a custom URI scheme, with tools to add notes and create styled summaries.418
- FlicenseNot gradedqualityDmaintenanceA Claude-compatible MCP server that enables storing and summarizing notes through a simple note storage system with custom URI scheme.5
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