ComfyUI MCP Server
Servidor MCP comfy-ui-mcp-server
Un servidor para conectarse a una interfaz de usuario cómoda local
Componentes
Recursos
El servidor implementa un sistema de almacenamiento de notas simple con:
Nota personalizada: esquema URI para acceder a notas individuales
Cada recurso de nota tiene un nombre, una descripción y un tipo MIME de texto simple.
Indicaciones
El servidor proporciona un único mensaje:
resumir-notas: Crea resúmenes de todas las notas almacenadas
Argumento opcional "estilo" para controlar el nivel de detalle (breve/detallado)
Genera un mensaje que combina todas las notas actuales con la preferencia de estilo.
Herramientas
El servidor implementa una herramienta:
add-note: Agrega una nueva nota al servidor
Toma "nombre" y "contenido" como argumentos de cadena requeridos
Actualiza el estado del servidor y notifica a los clientes sobre los cambios de recursos.
Related MCP server: Datetime MCP Server
Configuración
[TODO: Agregar detalles de configuración específicos para su implementación]
Inicio rápido
Instalar
Escritorio de Claude
En MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json En Windows: %APPDATA%/Claude/claude_desktop_config.json
Desarrollo
Construcción y publicación
Para preparar el paquete para su distribución:
Sincronizar dependencias y actualizar archivo de bloqueo:
uv syncDistribuciones de paquetes de compilación:
uv buildEsto creará distribuciones de origen y de rueda en el directorio dist/ .
Publicar en PyPI:
uv publishNota: Deberás configurar las credenciales de PyPI a través de variables de entorno o indicadores de comando:
Token:
--tokenoUV_PUBLISH_TOKENO nombre de usuario/contraseña:
--username/UV_PUBLISH_USERNAMEy--password/UV_PUBLISH_PASSWORD
Depuración
Dado que los servidores MCP se ejecutan en stdio, la depuración puede ser complicada. Para una experiencia óptima, recomendamos usar el Inspector MCP .
Puede iniciar el Inspector MCP a través de npm con este comando:
npx @modelcontextprotocol/inspector uv --directory E:\Claude\comfy-ui-mcp-server run comfy-ui-mcp-serverAl iniciarse, el Inspector mostrará una URL a la que podrá acceder en su navegador para comenzar a depurar.
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
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.
- TaprootOAuthcom.taproothq
Persistent memory layer for AI tools. Save and recall notes across Claude and other MCP clients.
Related MCP Servers
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- 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-