invokeai-mcp
This server bridges AI coding agents to a local InvokeAI creative engine, enabling full control over image generation, management, and system administration.
Image generation: Text-to-image (txt2img), image-to-image (img2img), inpainting (masked regions), and 4x RealESRGAN upscaling. Supports SD1.5, SDXL, Flux, and other model families. Configure parameters like prompt, negative prompt, model, seed, steps, dimensions (width/height), strength, CFG scale, and scheduler.
Queue management: Monitor queue status, list items, poll results, and download generated images. Cancel individual jobs, cancel batches, clear, pause, or resume the queue.
Gallery & image management: Browse, search by metadata, get details, download, star/unstar, and delete images.
Model management: List installed models by type, install from HuggingFace repo IDs, Civitai URLs, or local paths; update and delete models; track installation progress.
Board (collection) management: Create, list, get, update, and delete boards; add or remove images to/from boards.
Workflow management: List, view, save, and delete InvokeAI node workflows.
System introspection: Check InvokeAI health, version, configuration, cache statistics, and onboarding detection.
Dashboard cards: Display rich status cards for fleet, queue, models, and recent gallery images.
Utilities: Built-in help system with topic-based documentation; graceful server shutdown.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@invokeai-mcp Generate a photorealistic portrait of an astronaut riding a horse on Mars"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
InvokeAI MCP
A bridge between AI coding agents (Claude, Cursor, opencode) and your local InvokeAI creative engine - text-to-image, image-to-image, masked inpaint, and upscaling on your own GPU, plus full queue, model, gallery, board, and workflow management, with a polished dark webapp.
What this wraps
This repo wraps InvokeAI, the open-source professional Stable Diffusion /
Flux creative engine (Apache-2.0, 27k+ stars). InvokeAI runs as a local web
server (default http://127.0.0.1:9090) and is installed separately through
its launcher - it is never bundled here. See docs/WRAPPEE.md.
Related MCP server: ComfyUI MCP Hub
Preview
Dashboard | Generate |
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What You Can Do
How it runs: headless bridge - this MCP server talks to your running InvokeAI instance over its REST API. InvokeAI must be installed and running (launcher install, model download, first run). Nothing is bundled.
Direction | Artifacts | Notes |
Hands-in | prompts, negative prompts, images (img2img/inpaint), mask images, model sources | Text or uploaded images |
Hands-out | generated PNGs, image URLs, local file paths, queue state |
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Generate SD1.5 / SDXL / Flux / SD3.5 / Qwen Image images on your RTX 4090
8 generation modes with horizontal tabs: txt2img, img2img, inpaint, outpaint, upscale, ControlNet, IP-Adapter, seamless tiling
60-style catalog + 24 material presets with select-all batch generation and live per-item progress
AI prompt refiner (local LLM) that understands the selected style/material
Plugin surface: install custom node packs from git, browse 250+ built-in engine nodes
Transform existing images (img2img) and repair regions (masked inpaint)
4x RealESRGAN upscaling
Install models from HuggingFace or Civitai without leaving the chat
Full queue control: status, cancel, clear, resume, result polling
Gallery search, boards, star/favorite organization; sort/filter by style, painter, franchise, board, starred; batch ops (star/unstar/delete/zip/move) with exact per-image attribution
76 curated styles + 23 franchise presets + 60 painters + 1321 A1111 community style pack entries - all batchable in any combination
Workflow library management (save/load node workflows)
Dark SOTA webapp: Generate, Gallery, Models, Queue, Boards, Workflows, Inbox, Tools, Skills, Chat (local LLM), Settings, Help, Logs
Supported models (engine catalog, 2026)
Model | Family | VRAM (fp16) | License / gating | Notes |
FLUX.1 Dev / Schnell | flux | 12-16 GB (fp8 ~7 GB) | Gated (token) / schnell Apache-2.0 | Schnell: 4-step, structural quality |
FLUX.2 Dev / Klein 9B / 4B | flux2 | 18 GB / 9 GB / 4 GB | Gated (token) | Current flagship |
SD 3.5 Large / Medium | sd-3 | 16 GB / 5 GB | License accept + token | Rivals FLUX.1-dev |
SDXL + community (Juggernaut XL, RealVis) | sdxl | 8 GB | Mostly open | Default: Juggernaut XL v9 |
CogView4-6B | cogview4 | 12 GB (fp8 ~7 GB) | Apache-2.0, no gating | Great text rendering, bilingual |
Z-Image Turbo / Base | z-image | 12 GB (fp8 ~6 GB) | Permissive | Turbo is very fast |
Qwen-Image 20B | qwen-image | ~40 GB (fp8 ~20 GB) | Apache-2.0, no gating | Best prompt adherence; too heavy for 24 GB with desktop apps |
SD 1.5 / 2.x | sd-1 | 4 GB | Open | Legacy, fast iteration |
Krea 2, Ideogram 4, Anima, ERNIE | external | varies | Closed / API | Engine supports; not self-hostable |
Graph builders ship for sd-1, sdxl, flux, cogview4 (verified against engine 6.13.7);
sd-3, qwen-image, z-image, flux2 wiring is mechanical follow-up on request.
Models install to N:\InvokeAI-models (configurable via models_dir in invokeai.yaml).
Quick Install
The fastest path is the MCPB bundle for Claude Desktop:
Download
invokeai-mcp-0.1.0.mcpbfrom ReleasesOpen Claude Desktop and drag the file onto the window
Complete onboarding (install InvokeAI, download a model) - see docs/ONBOARDING.md
Other methods (mcpb CLI, manual config, webapp dev stack) are in INSTALL.md.
Example Prompts
"Generate a neon cyberpunk city at night, rain, cinematic lighting"
"Use this image and make it a watercolor painting: [gallery image]"
"What models do I have installed? Install SDXL base from HuggingFace"
"Show me my recent images and download the last one to disk"
Documentation
Doc | Contents |
All install methods, prerequisites | |
First-timer InvokeAI setup, model downloads, pitfalls | |
What InvokeAI is, official links, community | |
System architecture, graph builders, ports | |
Env vars, config options | |
All available tools | |
Contributing, local setup | |
Common issues |
Requirements
InvokeAI installed and running (launcher install; models downloaded) - free, Apache-2.0
GPU with 6-12 GB VRAM recommended (SD1.5: 4 GB, SDXL: 8 GB, Flux: 12 GB+)
Engine install:
invokeai-webmanual install (uv venv+uv pip install invokeai) or the official launcher; the engine serves its full canvas GUI on 9090Claude Desktop (or any MCP client) for chat use; a browser for the webapp
Python 3.12+ and uv for source installs; Node/Bun for the webapp
License
MIT
Available Tools
13 toolsinvokeai_boardsA
Manage boards (collections of generated images).
[RATIONALE] Boards are the primary organization surface of the InvokeAI gallery; all board operations share one record store and one portmanteau.
Return Format
{"success": bool, "operation": str, "data": {...}, "message": str}
Examples
invokeai_boards(operation="list") invokeai_boards(operation="create", board_name="Concept art") invokeai_boards(operation="add_image", board_id="board-uuid", image_names=["abc123.png"])
Notes:
Board ids come from invokeai_boards(operation='list').
Inputs: operation (required), board_id, board_name, image_names. Returns: object
invokeai_galleryA
Browse, search, download, and manage generated images.
[RATIONALE] The gallery is a single searchable feed (gallery endpoint) with per-image actions; one portmanteau keeps discovery and mutation together.
Return Format
{"success": bool, "operation": str, "data": {...}, "message": str, "images": [{"image_name", "url", "thumbnail_url", "width", "height"}]}
Examples
invokeai_gallery(operation="list", limit=20) invokeai_gallery(operation="search", query="cyberpunk") invokeai_gallery(operation="download", image_name="abc123.png")
Notes:
URLs returned are absolute (InvokeAI host), usable in chat/browser.
Inputs: operation (required), limit, query, offset, +3 more. Returns: object
invokeai_generateA
Generate images through the local InvokeAI creative engine.
[RATIONALE] All generation paths share one enqueue flow (graph build -> queue batch), so they live under a single portmanteau with an operation discriminator instead of four near-identical tools.
txt2img: text prompt to a fresh image.
img2img: transform an existing image (needs image_name, uses strength).
inpaint: regenerate a masked region (needs image_name + mask_image_name).
upscale: RealESRGAN 4x upscale of an existing image (needs image_name).
The job is enqueued and runs asynchronously; poll completion with invokeai_queue(operation="item_status" or "result").
Return Format
{"success": bool, "queue_item_id": int, "batch_id": str, "message": str, "poll": {"tool": "invokeai_queue", "args": {...}}}
Examples
invokeai_generate(operation="txt2img", prompt="neon cyberpunk city at night, rain") invokeai_generate(operation="img2img", prompt="make it a watercolor painting", image_name="abc123.png", strength=0.6) invokeai_generate(operation="upscale", image_name="abc123.png")
Notes:
InvokeAI must be running (onboarding) and a main model installed.
Model base determines graph family: sd-1, sdxl, flux.
Inputs: prompt (required), operation (required), runs, seed, +10 more. Returns: object
invokeai_helpA
Get documentation for this server's tools and workflows.
Return Format
{"success": bool, "help": str}
Examples
invokeai_help() invokeai_help(topic="tools")
Inputs: topic. Returns: object
invokeai_modelsA
Manage installed models in the local InvokeAI instance.
[RATIONALE] Model lifecycle (discovery, install from HF/Civitai, config, removal) is a single domain with a shared record store, so all operations live under this portmanteau.
Sources accepted by install: HuggingFace repo ids (e.g. "stabilityai/stable-diffusion-xl-base-1.0"), Civitai model URLs, or local paths. Install runs asynchronously; poll with operation='installs'.
Return Format
{"success": bool, "operation": str, "data": {...}, "message": str}
Examples
invokeai_models(operation="list", model_type="main") invokeai_models(operation="install", source="stabilityai/stable-diffusion-xl-base-1.0", config={"name": "SDXL Base"}) invokeai_models(operation="delete", key="sdxl-main")
Notes:
model_type values follow InvokeAI taxonomy (main, lora, vae, ...).
Install may download multiple GB; check operation='installs' for progress.
Inputs: operation (required), key, limit, config, +3 more. Returns: object
invokeai_queueA
Manage the InvokeAI generation queue and retrieve results.
[RATIONALE] Queue lifecycle (inspect, control, and harvest outputs) is one domain, so all operations share this portmanteau. 'result' is the natural partner of invokeai_generate: it polls an item to completion and returns the output image URLs (or downloads the file locally).
Return Format
{"success": bool, "operation": str, "data": {...}, "message": str}
Examples
invokeai_queue(operation="status") invokeai_queue(operation="list", status_filter="completed", limit=10) invokeai_queue(operation="result", item_id=123, wait_seconds=120, download_image=True) invokeai_queue(operation="cancel_batch", batch_ids=["batch-uuid"])
Notes:
result polls every 3s up to wait_seconds; use wait_seconds for synchronous flows.
Without wait_seconds, result returns the current item state immediately.
Inputs: operation (required), limit, item_id, batch_ids, +3 more. Returns: object
invokeai_shutdownA
Gracefully shut down this MCP server.
Return Format
{"success": bool, "message": str}
Examples
invokeai_shutdown()
Inputs: no named parameters. Returns: object
invokeai_systemA
Check InvokeAI connectivity, version, and runtime configuration.
[RATIONALE] System introspection (health, version, config, cache stats) is one domain and the standard first call for onboarding and debugging.
Return Format
{"success": bool, "operation": str, "data": {...}, "message": str, "configured": bool}
Examples
invokeai_system(operation="health") invokeai_system(operation="version")
Notes:
health returns configured=false when InvokeAI is not reachable (drives the webapp onboarding cue).
Inputs: operation (required). Returns: object
invokeai_workflowsA
Manage InvokeAI node workflows (list, get, save, delete).
[RATIONALE] Workflows are stored artifacts in a single store; CRUD plus export share this portmanteau. Running a workflow graph goes through invokeai_generate or a raw enqueue - this tool manages the stored workflow library.
Return Format
{"success": bool, "operation": str, "data": {...}, "message": str}
Examples
invokeai_workflows(operation="list") invokeai_workflows(operation="get", workflow_id="wf-uuid") invokeai_workflows(operation="save", workflow_json='{"nodes": {...}}')
Notes:
workflow_json must be a full InvokeAI workflow object (id, nodes, edges).
Save with an existing id updates; without one, creates.
Inputs: operation (required), limit, offset, workflow_id, +1 more. Returns: object
show_invokeai_dashboard_cardA
Show InvokeAI fleet status (version, queue, model counts) as a rich card.
Return Format
ToolResult with PrefabApp card; plain text fallback in content.
Examples
show_invokeai_dashboard_card()
Inputs: no named parameters.
show_invokeai_gallery_cardB
Show the most recent gallery images as a rich card.
Return Format
ToolResult with PrefabApp card; plain text fallback in content.
Examples
show_invokeai_gallery_card(limit=6)
Inputs: limit.
show_invokeai_models_cardA
Show installed models of a type as a rich card.
Return Format
ToolResult with PrefabApp card; plain text fallback in content.
Examples
show_invokeai_models_card(model_type="main")
Inputs: model_type.
show_invokeai_queue_cardA
Show the generation queue status as a rich card.
Return Format
ToolResult with PrefabApp card; plain text fallback in content.
Examples
show_invokeai_queue_card()
Inputs: no named parameters.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 13 tools
Each tool targets a distinct domain (queue, gallery, models, generation, workflows, boards, system), and the operation-based tools (invokeai_generate, invokeai_queue) are clearly separated. Minor overlap exists between show_invokeai_dashboard_card and invokeai_system (both report health/version), but descriptions mitigate confusion.
Naming is inconsistent: four tools use a 'show_invokeai_*_card' pattern while the rest use bare 'invokeai_*' nouns with operation parameters. Some tools are verb-like (invokeai_help, invokeai_shutdown), others are noun-objects (invokeai_gallery, invokeai_boards), making the pattern unpredictable.
13 tools is well within the ideal range for a server covering a rich creative domain. Each tool addresses a distinct area (generation, queueing, model management, gallery, workflows, system), and the card-based display tools serve as convenient views rather than redundant functionality.
Core workflows are well covered: generation includes txt2img, img2img, inpaint, and upscale; queue handles lifecycle and result retrieval; models support CRUD; gallery and boards manage images; workflows have full CRUD. Minor gaps exist (e.g., no explicit single-image delete outside 'manage' operations, and no batch generation interface), but agents can accomplish tasks without major dead ends.
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