io.github.pvliesdonk/image-generation-mcp
This server offers image generation and management across multiple AI providers: OpenAI (DALL-E), Google Gemini, Stable Diffusion WebUI, and a test placeholder. Key capabilities include:
Generate: create images from prompts, with automatic provider routing (
auto) based on content type.Monitor: poll
check_generation_statusfor long-running tasks.View & Transform: display images with on-demand resize, crop, format conversion via
image://URIs.Browse: explore gallery with pagination, thumbnails, and filtering by origin.
Edit: interactive crop, rotate, flip, saving as new images.
Input Images: upload or fetch images for image-to-image generation (e.g., background replacement).
Discovery: list providers, models, supported aspects, styles, and deprecation warnings.
Style Library: save/apply style presets.
Resources: list and read MCP resources.
Server Info: check version details.
Cost Confirm: require user approval for paid providers.
Generates images using Google Gemini models like gemini-2.5-flash-image and gemini-3.x previews.
Generates images using OpenAI models such as gpt-image-1.5, gpt-image-1, and dall-e-3.
Click on "Install 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., "@io.github.pvliesdonk/image-generation-mcpGenerate a sci-fi cityscape at night, 16:9."
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.
Image Generation MCP
Multi-provider image generation MCP server built on FastMCP. Generate images from Claude Desktop, Claude Code, or any MCP client using OpenAI, Google Gemini, Stable Diffusion (SD WebUI), or a zero-cost placeholder provider.
Documentation | Config wizard | PyPI | Docker
Features
Multi-provider: OpenAI (
gpt-image-2,gpt-image-1.5,dall-e-3), Google Gemini (gemini-3.1-flash-image,gemini-3-pro-image,gemini-3.1-flash-lite-image), SD WebUI (Stable Diffusion / Forge / reForge), and a zero-cost placeholder for testing.Per-model style metadata: every model carries a
style_profile(strengths, prompt grammar, lifecycle);list_providersincludes a top-levelwarningsarray for deprecated models. See Model Catalog.Keyword-based auto-selection:
provider="auto"routes by prompt content (text/logo → OpenAI, photoreal/anime → SD WebUI, draft → placeholder).CDN-style image transforms:
image://{id}/view?format=webp&width=512&crop_x=...resizes / re-encodes / crops on demand without re-generating.Hybrid background tasks: long-running SD generations run with
task=True(poll for status); short OpenAI calls stream progress in the foreground.MCP Apps gallery + viewer: interactive UI surfaces (browse generated images, edit / crop / rotate) for clients that support
app:resources.Production deployment: Docker (multi-arch),
.deb/.rpmwith hardened systemd, OIDC + bearer auth, persistent EventStore for HTTP session resumability.
Related MCP server: NanoBanana MCP
What you can do with it
With this server mounted in an MCP client, you can ask:
"Generate a coffee mug product photo on a worn oak table, 16:9, no text." Routes to
gpt-image-1.5for typography-aware photorealism."Create three concept-art variations of a cyberpunk alley at dusk." Composes
generate_imagewithprovider="sd_webui"and a stylised checkpoint likedreamshaperXL."Crop this image to a 1:1 square centred on the subject and resize to 512px." Uses
image://{id}/view?width=512&height=512&crop_x=...resource transforms."Show me my recent generations." Browses the gallery via the
image://listresource and the MCP Apps gallery viewer."Save this style as 'cyberpunk-night' so I can apply it to future requests." Uses the style library, whose markdown briefs the LLM interprets per-provider.
"Replace the background of my last photo with a sunset sky." Uses
transform_imagewith the galleryimage_idas a reference (image-to-image via Gemini).
Installation
From PyPI
pip install image-generation-mcpIf you add optional extras via the PROJECT-EXTRAS-START / PROJECT-EXTRAS-END sentinels in pyproject.toml, document them below:
Extra | Includes | Use when |
|
| Background-task support ( |
|
| Enables the OpenAI provider. |
|
| Enables the Gemini provider. |
|
| Everything except SD WebUI (which is HTTP-only, no extra needed). |
Example: pip install image-generation-mcp[all].
From source
git clone https://github.com/pvliesdonk/image-generation-mcp.git
cd image-generation-mcp
uv sync --all-extras --all-groupsDocker
docker pull ghcr.io/pvliesdonk/image-generation-mcp:latestA compose.yml ships at the repo root as a starting point. Copy .env.example to .env, edit, and docker compose up -d.
To attach a remote Python debugger (development only; the protocol is unauthenticated), see Remote debugging.
Linux packages (.deb / .rpm)
Download .deb or .rpm packages from the GitHub Releases page. Both install a hardened systemd unit; env configuration is sourced from /etc/image-generation-mcp/env (copy from the shipped /etc/image-generation-mcp/env.example).
Claude Desktop (.mcpb bundle)
Download the .mcpb bundle from the GitHub Releases page and double-click to install, or run:
mcpb install image-generation-mcp-<version>.mcpbClaude Desktop prompts for required env vars via a GUI wizard, with no manual JSON editing needed.
For manual Claude Desktop configuration and setup options, see Claude Desktop deployment.
Quick start
image-generation-mcp serve # stdio transport
image-generation-mcp serve --transport http --port 8000 # streamable HTTPFor library usage (embedding the domain logic without the MCP transport), import from the image_generation_mcp package directly. See the project's domain modules under src/image_generation_mcp/ for entry points.
Server info
The server registers a built-in get_server_info tool (via fastmcp_pvl_core.register_server_info_tool) so operators can confirm the deployed version with a single MCP call. The default response carries server_name, server_version, and core_version. Servers that talk to a remote upstream wire upstream version reporting inside the DOMAIN-UPSTREAM-START / DOMAIN-UPSTREAM-END sentinel in src/image_generation_mcp/server.py; see CLAUDE.md for the wiring pattern.
Configuration
Core environment variables shared across all fastmcp-pvl-core-based services:
Variable | Default | Description |
|
| Persistent-state backend URL shared by every pvl-core subsystem that needs state. |
|
| Log level for FastMCP internals and app loggers (DEBUG / INFO / WARNING / ERROR / CRITICAL). The -v CLI flag overrides to DEBUG. |
|
| Set false for plain or structured JSON log output. |
Domain-specific variables go below under Domain configuration.
Authentication
Callers authenticate via a bearer token or OIDC (mutually exclusive). See the Authentication guide for setup, mapped multi-subject tokens, OIDC, and troubleshooting.
Post-scaffold checklist
After copier copy and gh repo create --push:
Fill in the DOMAIN blocks (every section marked with a
DOMAINsentinel comment) in this README and inCLAUDE.md. TheGENERATED-ENV-TABLE-*regions are not DOMAIN blocks; the config generator owns them and rewrites them on every run.Configure GitHub secrets (see below).
Install dev + docs tooling:
uv sync --all-extras --all-groups.Install pre-commit hooks:
uv run pre-commit install.Run the gate locally:
uv run pytest -x -q && uv run ruff check --fix . && uv run ruff format . && uv run mypy src/ tests/.Push the first commit. CI should be green.
GitHub secrets
CI workflows reference three repository secrets. Configure them via Settings → Secrets and variables → Actions or with gh secret set:
Secret | Used by | How to generate |
|
| Fine-grained PAT at https://github.com/settings/personal-access-tokens/new with |
|
| https://codecov.io: sign in with GitHub and add the repo. The upload token is on its settings page. |
|
| Run |
gh secret set RELEASE_TOKEN
gh secret set CODECOV_TOKEN
gh secret set CLAUDE_CODE_OAUTH_TOKENDependency updates are handled by Renovate (
renovate.yml), which reusesRELEASE_TOKEN. It maintainsuv.lockand auto-merges patch/minor bumps once theCI Successcheck is green;bootstrap.ymlenables auto-merge and branch protection on first push. GitHub Actions are updated in the copier template and arrive viacopier update, not per-repo.
GITHUB_TOKEN is auto-provided; no action needed.
Local development
The PR gate (matches CI):
uv run pytest -x -q # tests
uv run ruff check --fix . && uv run ruff format . # lint + format
uv run mypy src/ tests/ # type-checkPre-commit runs a subset of the gate on each commit; see .pre-commit-config.yaml for details, or CLAUDE.md for the full Hard PR Acceptance Gates.
Troubleshooting
Moving a scaffolded project
uv sync creates .venv/bin/* scripts with absolute shebangs pointing at the venv Python. If you move the repo after scaffolding (mv /old/path /new/path), uv run pytest fails with ModuleNotFoundError: No module named 'fastmcp' because the stale shebang resolves to a different interpreter than the venv's site-packages.
Fix:
rm -rf .venv
uv sync --all-extras --all-groupsuv run python -m pytest also works as a one-shot workaround (bypasses the stale entry-script shim).
uv.lock refresh after copier update
When copier update introduces new dependencies (such as a new extra added to pyproject.toml.jinja), CI runs uv sync --frozen which fails against a stale lockfile. Run uv lock locally and commit the refreshed uv.lock alongside accepting the copier-update PR.
Links
Domain configuration
Domain environment variables use the IMAGE_GENERATION_MCP_ prefix:
Variable | Default | Required | Description |
| (none) | No | Deprecated alias for IMAGE_GENERATION_MCP_SD_WEBUI_HOST; logs a warning when used. |
| (none) | No | Deprecated alias for IMAGE_GENERATION_MCP_SD_WEBUI_MODEL; logs a warning when used. |
|
| No | When true, write-tagged tools (image generation, transforms, uploads) are hidden from clients. Set false to enable them. |
|
| No | Directory where generated images are saved. Created automatically on first use. |
| (none) | No | OpenAI API key. Enables the OpenAI provider (gpt-image-2, gpt-image-1.5, dall-e-3) when set. |
| (none) | No | Google API key. Enables the Gemini provider (gemini-3.1-flash-image and others) when set. Get a key at https://aistudio.google.com/apikey. |
| (none) | No | SD WebUI base URL (such as http://localhost:7860). Enables the SD WebUI provider when set. Compatible with AUTOMATIC1111, Forge, reForge, and Forge-neo. |
| (none) | No | SD WebUI checkpoint name, used for model-aware preset detection (SD 1.5 / SDXL / Lightning) and checkpoint override. Unset uses the instance's current model. |
|
| No | Provider used when no keyword triggers auto-selection: auto, openai, gemini, sd_webui, or placeholder. auto picks the first configured provider. |
|
| No | Maximum number of transformed image results (resize, crop, convert) kept in memory. Set 0 to disable caching. |
|
| No | Comma-separated provider names that cost money; generate_image asks for confirmation (client elicitation) before using them. An empty value falls back to this default; to disable confirmation, set a value that names no provider (such as none). |
|
| No | Directory for style preset files (Markdown with YAML front matter). Created automatically if it does not exist. |
|
| No | Allow reading input images from local filesystem paths. Off by default: only URLs and uploads are accepted. |
|
| No | Maximum accepted input image size in bytes. |
|
| No | HTTP timeout in seconds when fetching remote image URLs (fetch_image and URL inputs). |
|
| No | Link lifetime in seconds when the caller requests no explicit TTL. |
|
| No | Ceiling in seconds a caller-requested link TTL is clamped to. |
|
| No | Post-success grace window in seconds: a served token's TTL shrinks to this so a stalled transfer can retry within it. |
|
| No | Crashed-handler reclaim window in seconds for an in-flight reservation. |
|
| No | Maximum size in bytes of a single upload. |
The create_download_link / create_upload_link tools and the /transfer/{token} route register only on an HTTP or SSE transport with BASE_URL set, and store link tokens in IMAGE_GENERATION_MCP_KV_STORE_URL; the IMAGE_GENERATION_MCP_TRANSFER_* knobs above tune link lifetime and upload limits. Security: IMAGE_GENERATION_MCP_ALLOW_LOCAL_FILE_INPUT grants callers server-filesystem read access via reference-image paths; enable it only for trusted callers or local single-user deployments.
Domain-config fields are composed inside src/image_generation_mcp/config.py between the CONFIG-FIELDS-START / CONFIG-FIELDS-END sentinels; env reads go through fastmcp_pvl_core.env(_ENV_PREFIX, "SUFFIX", default) so naming stays consistent, and field invariants go in __post_init__ between the CONFIG-VALIDATE-START / CONFIG-VALIDATE-END sentinels. Each field's metadata help and tags generate the table above directly, so keep them accurate and complete.
Key design decisions
Multi-provider with capability discovery, not feature flags. Each provider's
discover_capabilities()reports its actual supported aspect ratios / qualities / formats / negative-prompt support at startup; routing logic asks the capability surface, not a hard-coded enum. New providers slot in by satisfying the protocol, with no router edits needed. (Seedocs/decisions/0001-…,0002-…,0007-….)Per-model
style_profilemetadata, surfaced vialist_providers. Closed-list providers (OpenAI, Gemini, placeholder) use exact-key lookup; SD WebUI uses a regex-ordered pattern table. Profiles include lifecycle flags (current/legacy/deprecated) and feed an auto-built top-levelwarningsarray. (Seedocs/decisions/0009-….)Hybrid background tasks. Short calls (OpenAI ~5 s) stream progress in-line; long calls (SD WebUI 30-180 s) run as background tasks with
check_generation_statuspolling; clients pick the mode viatask=True. (Seedocs/decisions/0005-….)Image asset model: content-addressed registry + sidecar JSON metadata + on-demand transforms. Generated images keep their full-resolution original;
image://{id}/view?format=webp&width=512&crop_x=…resources do format conversion / resize / crop on demand without re-generating. Transforms are cached. (Seedocs/decisions/0006-….)Style library. User-saved markdown briefs (with YAML frontmatter for tags / aspect ratio / quality) that the LLM interprets per-provider, not copy-pasted verbatim. Distinct from per-model
style_profile: style library is the brief;style_profiledescribes the model. (Seedocs/decisions/0008-…and0009-…for disambiguation.)Composes
fastmcp_pvl_core.ServerConfig, never inherits. Domain config goes betweenCONFIG-FIELDS-START/CONFIG-FIELDS-ENDsentinels; env reads route throughfastmcp_pvl_core.env(...)to keep prefix naming consistent.
Maintenance
Resources
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