ComfyUI MCP Server
comfy-ui-mcp-server MCP サーバー
ローカルcomfyUIに接続するためのサーバー
コンポーネント
リソース
サーバーは、次の機能を備えたシンプルなメモ保存システムを実装します。
個々のノートにアクセスするためのカスタム note:// URI スキーム
各ノートリソースには、名前、説明、text/plain mimetype があります。
プロンプト
サーバーは単一のプロンプトを提供します:
要約ノート: 保存されているすべてのノートの要約を作成します
詳細レベル(簡潔/詳細)を制御するためのオプションの「スタイル」引数
現在のすべてのメモをスタイルの好みに合わせて組み合わせたプロンプトを生成します
ツール
サーバーは 1 つのツールを実装します。
add-note: サーバーに新しいメモを追加します
必須の文字列引数として「name」と「content」を取ります
サーバーの状態を更新し、リソースの変更をクライアントに通知します
Related MCP server: Datetime MCP Server
構成
[TODO: 実装に固有の構成の詳細を追加する]
クイックスタート
インストール
クロードデスクトップ
MacOS の場合: ~/Library/Application\ Support/Claude/claude_desktop_config.json Windows の場合: %APPDATA%/Claude/claude_desktop_config.json
発達
建築と出版
配布用のパッケージを準備するには:
依存関係を同期し、ロックファイルを更新します。
uv syncパッケージディストリビューションをビルドします。
uv buildこれにより、 dist/ディレクトリにソースとホイールのディストリビューションが作成されます。
PyPI に公開:
uv publish注: 環境変数またはコマンド フラグを使用して PyPI 資格情報を設定する必要があります。
トークン:
--tokenまたはUV_PUBLISH_TOKENまたはユーザー名/パスワード:
--username/UV_PUBLISH_USERNAMEおよび--password/UV_PUBLISH_PASSWORD
デバッグ
MCPサーバーはstdio経由で実行されるため、デバッグが困難になる場合があります。最適なデバッグ環境を実現するには、 MCP Inspectorの使用を強くお勧めします。
次のコマンドを使用して、 npm経由で MCP Inspector を起動できます。
npx @modelcontextprotocol/inspector uv --directory E:\Claude\comfy-ui-mcp-server run comfy-ui-mcp-server起動すると、ブラウザでアクセスしてデバッグを開始できる URL がインスペクタに表示されます。
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.
Maintenance
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