Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Descripción del creador (original en inglés)
MCP server + Claude Code plugin for ComfyUI: run workflows, generate images, manage models & VRAM.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"comfyui": {
"command": "npx",
"args": [
"-y",
"comfyui-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add comfyui -- npx -y comfyui-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"comfyui": {
"command": "npx",
"args": [
"-y",
"comfyui-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"comfyui": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"comfyui-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 8 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub artokun, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.52.206) llegó al registro hace 24 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Figma Context MCP, Claude Real Video, Figma MCP Server
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
The Figma MCP server brings Figma design context directly into your AI workflow.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
The Agent panel driving ComfyUI end to end. It reads what is installed locally, wires the graph, frees VRAM, and runs the render.
Watch the 76s demo →
The local-first, agent-native control plane for [ComfyUI](https://github.com/comfyanonymous/ComfyUI). An MCP server plus a live sidebar agent that generates images, video and audio, authors and runs workflows, manages models and custom nodes, and edits your live ComfyUI graph in natural language. Bring whatever model you have: Claude or ChatGPT on your subscription, Gemini on your Google login, a free local model via Ollama (fully offline), or any hosted model over one API key (DeepSeek, GLM, MiMo, Kimi, GPT, Claude via OpenRouter). The tools and the panel are the same on every tier, and the built-in LLM Arena scores each model on real ComfyUI tasks so you know what yours can do. One config targets local installs, LAN, VPS, or Comfy Cloud.
One-click GPU pod. A ready-to-run ComfyUI with this project + Agent Panel + ComfyUI-Manager v2 preinstalled, on your own GPU. No setup.
Works on macOS, Linux, and Windows. Auto-detects your ComfyUI installation and port.
Stuck or have a question? [Join the Discord](https://discord.gg/cW9arBhzCu) for help, model tips, and release announcements.
38 MCP tools | 42 AI skills (Flux · WAN · LTX 2.3 video · MiniMax H3 · Qwen · Z-Image · Ideogram 4 · ERNIE · ANIMA · model registry · Civitai · node authoring · launch/perf flags) | 56 installer packs | 11 slash commands | 4 autonomous agents | 3 hooks
The plugin ships skills that grow with every release: model-specific generation guides with curated download URLs, workflow recipes, troubleshooting, and custom-node authoring. With them, Claude knows the right sampler, CFG, resolution, and model files for each architecture without trial and error.
### Now available: the ComfyUI Agent Panel on ComfyUI-Manager & the Comfy Registry
An autonomous AI agent in your ComfyUI sidebar, running on Claude, ChatGPT, Gemini, or ANY local/hosted LLM (Ollama and every OpenAI-compatible endpoint). Subscriptions work with no API key; local models work with no account at all. Pick a provider and it drives your live graph: edits, spatial layout, one-shot workflow/pack loads, rewind/rollback, a pending-message tray, activity cards, multi-tab. It asks before spending paid API credits.
Search
comfyui-agent-panelin ComfyUI-Manager to install. Read more →
Full documentation: [comfyui-mcp.artokun.io/docs](https://comfyui-mcp.artokun.io/docs)
1. Install ComfyUI (if you haven't already): ComfyUI Desktop or from source
2. Add the MCP server to your Claude Code config (~/.claude/settings.json):
{
"mcpServers": { "comfyui": { "command": "npx", "args": ["-y", "comfyui-mcp"], "env": { "CIVITAI_API_TOKEN": "" } } } }
3. Start using it. With ComfyUI running, ask Claude to generate an image:
> Generate an image of a sunset over mountains
Claude will find (or download) a checkpoint, build a workflow, execute it, and return the image.
Note: This runs as a standalone MCP server, so there is no need to clone this repo.
npxdownloads and runs it.
comfyui-mcp is local-first. A self-hosted ComfyUI on Mac/Linux/Windows is the primary target, and the same agent reaches remote installs (RunPod, VPS, LAN, reverse-proxied) from one config. Local-first, not local-only.
More than a bridge. Most ComfyUI MCP servers are thin connectors. They forward a prompt and hand back an image. comfyui-mcp is a full control plane. It authors and edits the graph node by node, runs and iterates on workflows, manages models and custom nodes, and ships model-specific expertise (samplers, CFG, resolutions, curated model URLs) so the agent gets it right without trial and error. If you want a minimal local relay, a lightweight server is fine. If you want an agent that *operates* ComfyUI, that's this project.
For Comfy Cloud users, Comfy-Org ships its own agent tooling: Comfy Cloud MCP (public beta, hosted on Comfy Cloud GPUs), the Comfy In-App Agent (private alpha, inside Comfy Cloud), and a first-party Comfy Local MCP (private test, not publicly available yet), all maintained by the Comfy team. If you don't have a GPU or you want zero setup, that's the better path; go use it.
Where this project differs is that it runs on your install and your choice of model, including a free local one via Ollama with no account and no network at all. comfyui-mcp *also* includes a community cloud mode (set COMFYUI_API_KEY, see Deployment modes) so a single MCP can target all three deployment shapes from one config.
[Local vs. Comfy Cloud agent](https://comfyui-mcp.artokun.io/docs/local-vs-comfy-cloud) is a side-by-side comparison, including when Comfy Cloud is the right answer. *(Statuses above are as of July 2026. Comfy-Org ships fast, so check their docs for the current state.)*
To use comfyui-mcp from Claude Desktop's Custom Connectors or any remote client, like Comfy's own cloud.comfy.org/mcp connector, run it as an authenticated, publicly reachable Streamable-HTTP server with one flag:
npx -y comfyui-mcp@latest --tunnel
This forces the HTTP transport, generates an auth token, opens a cloudflared quick tunnel, and prints a ready-to-paste https://…/mcp URL + token + Claude Desktop connector snippet. Auth accepts Authorization: Bearer or X-API-Key: (matching Comfy Cloud's convention). See the Remote / hosted connector guide for the full walkthrough and headless usage.
Auth is opt-in. With no
COMFYUI_MCP_HTTP_TOKENset and no--tunnel, the
default stdio (and plain
--httpon loopback) behavior is unchanged. It stays open and
local. OAuth (Comfy's browser sign-in flow) is a planned follow-up.
This package also ships as a Claude Code plugin that adds slash commands, skills, agents, and hooks on top of the MCP tools.
# In Claude Code
/plugin marketplace add artokun/comfyui-mcp /plugin install comfy
| Command | Description |
|---|---|
/comfy:gen | Generate an image from a text description — auto-selects checkpoint, builds workflow, returns image |
/comfy:viz | Visualize a workflow as a Mermaid diagram with nodes grouped by category |
/comfy:node-skill | Generate a Claude skill for a custom node pack from Registry ID or GitHub URL |
/comfy:debug [prompt_id] | Diagnose why a workflow failed — reads history, logs, traces root cause, suggests fixes |
/comfy:batch | Parameter sweep generation across cfg, sampler, steps, seed, etc. |
/comfy:convert | Convert between UI format and API format workflows |
/comfy:install | Install a custom node pack — git clone, pip install, optional restart |
/comfy:gallery [filter] | Browse generated outputs with metadata — filter by date, count, or filename |
/comfy:compare | Diff two workflows side by side — shows added/removed nodes and changed parameters |
/comfy:recipe | Multi-step recipes: portrait, hires-fix, style-transfer, product-shot |
42 skills total: model-family guides (Flux, WAN, LTX 2.3, MiniMax H3, Qwen, Z-Image, Ideogram 4, ERNIE, ANIMA + anime / WAN / Z-Image LoRA training), the model-registry (curated download URLs), the civitai pairing skill, node authoring, the launch/performance-flags matrix, and the core four below. Full list on the plugin docs page.
Installer packs.
packs/bundles 13 one-command ComfyUI setups: ANIMA, Ideogram 4, LTX-2.3, ERNIE, WAN (animate / longer-videos / transparent), Qwen (image / image-edit), Z-Image (turbo / base / xy-plot) and artokun-flow (WAN Animate: replace / animate). Each is a manifest of custom nodes + model URLs + workflow that drives bothapply_manifestand generatedinstall-windows.bat/install-runpod.sh, with CI that validates every model link + payload size. Seepacks/README.md.
| Skill | Description |
|---|---|
| comfyui-core | Workflow format, node types, data flow patterns, pipeline architecture, MCP tool usage guide |
| prompt-engineering | CLIP weight syntax (word:1.3), BREAK tokens, embeddings, model-specific prompting for SD1.5/SDXL/Flux/SD3 |
| troubleshooting | Common error catalog — OOM, dtype mismatches, missing nodes, NaN tensors, black images, CUDA errors, with VRAM estimates per model |
| model-compatibility | Compatibility matrix — loaders, resolutions, CFG, samplers, ControlNets, LoRAs, and VAEs per model family (SD1.5/SDXL/Turbo/Lightning/Flux/SD3/LTXV) |
| Agent | Model | Description |
|---|---|---|
| comfy-explorer | Sonnet | Researches custom node packs — reads docs, queries /object_info, generates skill files |
| comfy-debugger | Sonnet | Autonomously diagnoses workflow failures — gathers logs + history, identifies failing node, checks models + custom nodes, proposes and optionally applies fixes |
| comfy-optimizer | Sonnet | Analyzes workflows for performance — detects redundant nodes, VRAM waste, wrong CFG/steps for model family, precision issues, suggests optimizations |
| comfy-researcher | Sonnet | Discovers and ranks ComfyUI custom node packs for a stated image-generation problem |
| Event | Trigger | Action |
|---|---|---|
| PreToolUse | enqueue_workflow | VRAM watchdog — checks GPU memory via /system_stats and warns if < 1GB free before execution |
| PreToolUse | restart_comfyui (actions stop/restart) | Save warning — prompts user to save unsaved workflow changes before stopping ComfyUI |
| PostToolUse | Any comfyui tool | Job completion notify — checks for completed jobs and injects compl
Solo se muestra el principio; lee el resto en el repo