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ComfyUI MCP Server

by jonpojonpo

comfy-ui-mcp-server MCP 服务器

用于连接到本地 comfyUI 的服务器

成分

资源

服务器实现了一个简单的笔记存储系统,其中包括:

  • 用于访问单个笔记的自定义 note:// URI 方案

  • 每个笔记资源都有一个名称、描述和文本/纯文本 mimetype

提示

服务器提供一个提示:

  • 总结笔记:创建所有存储笔记的摘要

    • 可选的“样式”参数用于控制详细程度(简要/详细)

    • 生成提示,结合所有当前注释和样式偏好

工具

服务器实现了一个工具:

  • add-note:向服务器添加新注释

    • 将“name”和“content”作为必需的字符串参数

    • 更新服务器状态并通知客户端资源变化

Related MCP server: Datetime MCP Server

配置

[TODO:添加特定于您的实现的配置详细信息]

快速入门

安装

克劳德桌面

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

发展

构建和发布

准备分发包:

  1. 同步依赖项并更新锁文件:

uv sync
  1. 构建软件包分发版:

uv build

这将在dist/目录中创建源和轮子分布。

  1. 发布到 PyPI:

uv publish

注意:您需要通过环境变量或命令标志设置 PyPI 凭据:

  • 令牌: --token或UV_PUBLISH_TOKEN

  • 或用户名/密码: --username / UV_PUBLISH_USERNAME和--password / UV_PUBLISH_PASSWORD

调试

由于 MCP 服务器通过 stdio 运行,调试起来可能颇具挑战性。为了获得最佳调试体验,我们强烈建议使用MCP Inspector 。

您可以使用以下命令通过npm启动 MCP Inspector:

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

启动后,检查器将显示一个 URL,您可以在浏览器中访问该 URL 以开始调试。

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • Addedgenerate_image

TDQS

C2.9/5.0

Scored across 1 tool

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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