io.github.pvliesdonk/image-generation-mcp
Multi-provider image generation MCP server that enables image generation from Claude Desktop, Claude Code, or any MCP client using OpenAI, Google Gemini, Stable Diffusion, or a placeholder provider.
README
<!-- mcp-name: io.github.pvliesdonk/image-generation-mcp -->
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 | PyPI | Docker
Features
<!-- DOMAIN-START -->
- Multi-provider — OpenAI (
gpt-image-1.5,gpt-image-1,dall-e-3), Google Gemini (gemini-2.5-flash-image,gemini-3.xpreviews), 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. <!-- DOMAIN-END -->
What you can do with it
<!-- DOMAIN-START -->
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 — markdown briefs the LLM interprets per-provider. <!-- DOMAIN-END -->
<!-- ===== TEMPLATE-OWNED SECTIONS BELOW — DO NOT EDIT; CHANGES WILL BE OVERWRITTEN ON COPIER UPDATE ===== -->
Installation
From PyPI
pip install image-generation-mcp
If you add optional extras via the PROJECT-EXTRAS-START / PROJECT-EXTRAS-END sentinels in pyproject.toml, document them below:
<!-- DOMAIN-START -->
| Extra | Includes | Use when |
|---|---|---|
mcp |
fastmcp[tasks]>=3.0,<4 |
Background-task support (task=True) — required for long SD generations. |
openai |
openai>=1.0 |
Enables the OpenAI provider. |
google-genai |
google-genai>=1.0 |
Enables the Gemini provider. |
all |
fastmcp[tasks] + openai + google-genai |
Everything except SD WebUI (which is HTTP-only — no extra needed). |
Example: pip install image-generation-mcp[all].
<!-- DOMAIN-END -->
From source
git clone https://github.com/pvliesdonk/image-generation-mcp.git
cd image-generation-mcp
uv sync --all-extras --dev
Docker
docker pull ghcr.io/pvliesdonk/image-generation-mcp:latest
A compose.yml ships at the repo root as a starting point — copy .env.example to .env, edit, and docker compose up -d.
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>.mcpb
Claude Desktop prompts for required env vars via a GUI wizard — no manual JSON editing needed.
Quick start
image-generation-mcp serve # stdio transport
image-generation-mcp serve --transport http --port 8000 # streamable HTTP
For library usage (embedding the domain logic without the MCP transport), import from the image_generation_mcp package directly — see src/image_generation_mcp/domain.py for the entry point scaffold.
Configuration
Core environment variables shared across all fastmcp-pvl-core-based services:
| Variable | Default | Description |
|---|---|---|
FASTMCP_LOG_LEVEL |
INFO |
Log level for FastMCP internals and app loggers (DEBUG / INFO / WARNING / ERROR). The -v CLI flag overrides to DEBUG. |
FASTMCP_ENABLE_RICH_LOGGING |
true |
Set to false for plain / structured JSON log output. |
IMAGE_GENERATION_MCP_EVENT_STORE_URL |
memory:// |
Event store backend for HTTP session persistence — memory:// (dev), file:///path (survives restarts). |
Domain-specific variables go below under Domain configuration.
Post-scaffold checklist
After copier copy and gh repo create --push:
- Fill in the DOMAIN blocks in this README (Features, What you can do with it, Domain configuration, Key design decisions) and in
CLAUDE.md. - Configure GitHub secrets — see below.
- Install dev dependencies:
uv sync --all-extras --dev. - 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 |
|---|---|---|
RELEASE_TOKEN |
release.yml, copier-update.yml |
Fine-grained PAT at https://github.com/settings/personal-access-tokens/new with contents: write and pull_requests: write (the copier-update cron opens PRs). Scoped to this repo. |
CODECOV_TOKEN |
ci.yml |
https://codecov.io — sign in with GitHub, add the repo, copy the upload token from the repo settings page. |
CLAUDE_CODE_OAUTH_TOKEN |
claude.yml, claude-code-review.yml |
Run claude setup-token locally and paste the result. |
gh secret set RELEASE_TOKEN
gh secret set CODECOV_TOKEN
gh secret set CLAUDE_CODE_OAUTH_TOKEN
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-check
Pre-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 --dev
uv 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 (e.g. 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
<!-- ===== TEMPLATE-OWNED SECTIONS END ===== -->
Domain configuration
<!-- DOMAIN-START -->
All domain environment variables use the IMAGE_GENERATION_MCP_ prefix.
Core
| Variable | Default | Required | Description |
|---|---|---|---|
IMAGE_GENERATION_MCP_SCRATCH_DIR |
~/.image-generation-mcp/images/ |
No | Directory for saved generated images. |
IMAGE_GENERATION_MCP_READ_ONLY |
true |
No | Hide write-tagged tools (generate_image). Set to false to enable generation. |
IMAGE_GENERATION_MCP_DEFAULT_PROVIDER |
auto |
No | Default provider: auto, openai, gemini, sd_webui, placeholder. |
Providers
| Variable | Default | Required | Description |
|---|---|---|---|
IMAGE_GENERATION_MCP_OPENAI_API_KEY |
— | No | OpenAI API key; enables OpenAI provider when set. |
IMAGE_GENERATION_MCP_GOOGLE_API_KEY |
— | No | Google API key with Gemini access; enables Gemini provider when set. |
IMAGE_GENERATION_MCP_SD_WEBUI_HOST |
— | No | SD WebUI URL (e.g. http://localhost:7860); enables SD WebUI provider when set. Deprecated alias: A1111_HOST. |
IMAGE_GENERATION_MCP_SD_WEBUI_MODEL |
— | No | SD WebUI checkpoint name for preset detection and override. Deprecated alias: A1111_MODEL. |
Authentication
| Variable | Default | Required | Description |
|---|---|---|---|
IMAGE_GENERATION_MCP_BEARER_TOKEN |
— | No | Static bearer token; enables bearer auth when set. |
IMAGE_GENERATION_MCP_BASE_URL |
— | No | Public base URL for OIDC and MCP File Exchange downloads (e.g. https://mcp.example.com). |
IMAGE_GENERATION_MCP_OIDC_CONFIG_URL |
— | No | OIDC discovery endpoint URL. |
IMAGE_GENERATION_MCP_OIDC_CLIENT_ID |
— | No | OIDC client ID. |
IMAGE_GENERATION_MCP_OIDC_CLIENT_SECRET |
— | No | OIDC client secret. |
IMAGE_GENERATION_MCP_OIDC_JWT_SIGNING_KEY |
ephemeral | Yes on Linux/Docker | JWT signing key. |
IMAGE_GENERATION_MCP_OIDC_AUDIENCE |
— | No | Expected JWT audience claim. |
IMAGE_GENERATION_MCP_OIDC_REQUIRED_SCOPES |
openid |
No | Comma-separated required scopes. |
IMAGE_GENERATION_MCP_OIDC_VERIFY_ACCESS_TOKEN |
false |
No | Verify access token as JWT instead of id token. |
Cost control & performance
| Variable | Default | Required | Description |
|---|---|---|---|
IMAGE_GENERATION_MCP_PAID_PROVIDERS |
openai,gemini |
No | Comma-separated paid provider names. Triggers elicitation confirmation on capable clients. Set to empty to disable. |
IMAGE_GENERATION_MCP_TRANSFORM_CACHE_SIZE |
64 |
No | Max cached transforms. Set to 0 to disable caching. |
File Exchange (MCP downloads)
| Variable | Default | Required | Description |
|---|---|---|---|
IMAGE_GENERATION_MCP_FILE_EXCHANGE_ENABLED |
true on http/sse, false on stdio |
No | Master switch for the file-exchange producer. Set false to suppress all file_ref publishing. |
IMAGE_GENERATION_MCP_FILE_EXCHANGE_TTL |
3600 |
No | Default and maximum TTL (seconds) for published files and download URLs. create_download_link's ttl_seconds is clamped to this. |
IMAGE_GENERATION_MCP_FILE_EXCHANGE_CONSUME |
true |
Recommended false |
Master switch for the consumer side. This server is producer-only; set false to silence the upstream "consume on, no consumer_sink wired" startup warning. |
Server identity
| Variable | Default | Required | Description |
|---|---|---|---|
IMAGE_GENERATION_MCP_SERVER_NAME |
image-generation-mcp |
No | Server name shown to MCP clients. |
IMAGE_GENERATION_MCP_INSTRUCTIONS |
(dynamic) | No | System instructions for LLM context. |
IMAGE_GENERATION_MCP_HTTP_PATH |
/mcp |
No | HTTP endpoint mount path. |
IMAGE_GENERATION_MCP_APP_DOMAIN |
(auto) | No | MCP Apps widget sandbox domain. Auto-computed from BASE_URL for Claude; override for other hosts. |
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.
For the full MCP tool / resource / prompt surface and per-provider setup notes, see the documentation site. <!-- DOMAIN-END -->
Key design decisions
<!-- DOMAIN-START -->
- 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 implementing the protocol — 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. <!-- DOMAIN-END -->
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.