ComfyUI MCP Server
comfy-ui-mcp-server MCP 서버
로컬 comfyUI에 연결하기 위한 서버
구성 요소
자원
서버는 다음을 사용하여 간단한 메모 저장 시스템을 구현합니다.
개별 노트에 액세스하기 위한 사용자 지정 노트:// URI 체계
각 노트 리소스에는 이름, 설명 및 텍스트/일반 MIME 유형이 있습니다.
프롬프트
서버는 단일 프롬프트를 제공합니다.
summarize-notes: 저장된 모든 노트의 요약을 생성합니다.
세부 수준(간략/상세)을 제어하기 위한 선택적 "스타일" 인수
현재 모든 노트와 스타일 선호도를 결합하여 프롬프트를 생성합니다.
도구
서버는 하나의 도구를 구현합니다.
add-note: 서버에 새 메모를 추가합니다.
필수 문자열 인수로 "name"과 "content"를 사용합니다.
서버 상태를 업데이트하고 클라이언트에게 리소스 변경 사항을 알립니다.
Related MCP server: Datetime MCP Server
구성
[TODO: 구현에 맞는 구성 세부 정보 추가]
빠른 시작
설치하다
클로드 데스크탑
MacOS의 경우: ~/Library/Application\ Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
개발
건축 및 출판
배포를 위해 패키지를 준비하려면:
종속성 동기화 및 잠금 파일 업데이트:
지엑스피1
패키지 배포 빌드:
uv build이렇게 하면 dist/ 디렉토리에 소스와 휠 배포판이 생성됩니다.
PyPI에 게시:
uv publish참고: 환경 변수나 명령 플래그를 통해 PyPI 자격 증명을 설정해야 합니다.
토큰:
--token또는UV_PUBLISH_TOKEN또는 사용자 이름/비밀번호:
--username/UV_PUBLISH_USERNAME및--password/UV_PUBLISH_PASSWORD
디버깅
MCP 서버는 stdio를 통해 실행되므로 디버깅이 어려울 수 있습니다. 최상의 디버깅 환경을 위해서는 MCP Inspector 사용을 강력히 권장합니다.
다음 명령을 사용하여 npm 통해 MCP Inspector를 시작할 수 있습니다.
npx @modelcontextprotocol/inspector uv --directory E:\Claude\comfy-ui-mcp-server run comfy-ui-mcp-serverInspector를 실행하면 브라우저에서 접근하여 디버깅을 시작할 수 있는 URL이 표시됩니다.
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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- Added
generate_image
TDQS
Scored across 1 tool
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.
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