maginary-mcp

Model Context Protocol server for Maginary — enumerate the prompt-DSL flags the engine accepts, kick off generations, and poll for results, all from inside your MCP-compatible client (Claude Desktop, Cursor, Continue, custom).

why

Maginary uses a Midjourney-style --flag prompt DSL over an async HTTP API. This server:

• surfaces the full parameter catalog to your LLM so it can pick the right flags
• offers a one-shot generate tool that hits POST /api/gens/
• offers get_generation + wait_for_generation for polling to a terminal state
• works offline for the catalog tools (ships a bundled snapshot; refreshed from the live docs endpoint at startup when reachable)

connect

This is an MCP server — you don't run it directly; your AI client (Claude Desktop, Cursor, etc.) launches and talks to it behind the scenes. Just add one config block and start chatting.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the equivalent on your OS:

{
  "mcpServers": {
    "maginary": {
      "command": "uvx",
      "args": ["maginary-mcp"]
    }
  }
}

Restart Claude Desktop. Ask it to generate an image — it will see Maginary's tools automatically.

No account yet? No problem — Claude will walk you through signup (just give it your email). Already have an API key? Add it to skip that step:

"env": { "MAGINARY_API_KEY": "sk-mag-…" }

Requires Python 3.10+ and uv. Alternatively: pip install maginary-mcp.

configuration

Environment variables (all optional for local use):

hosted (no-install) — Streamable HTTP

Connect a client straight to the hosted server at https://mcp.maginary.ai/mcp. Zero install — the server is multi-tenant, so each request is scoped to whatever credential it arrives with. Two ways to authenticate, pick whichever fits the client:

Connect (OAuth) — for Claude Desktop, claude.ai, and any other client that speaks MCP's OAuth spec. Add the server with no headers at all:

{
  "mcpServers": {
    "maginary": { "url": "https://mcp.maginary.ai/mcp" }
  }
}

Click "Connect" in the client. It opens a login page on app.maginary.ai, you sign in and approve the requested scopes, and the client holds the token from then on — no key to generate or paste. Requires the server to be running with MAGINARY_MCP_REQUIRE_AUTH=1; without it, no login is asked for at all.

API key — for any client that doesn't do the OAuth dance (or if you'd rather not click through a login), generate a key at app.maginary.ai/dashboard#api-keys and send it yourself:

{
  "mcpServers": {
    "maginary": {
      "url": "https://mcp.maginary.ai/mcp",
      "headers": { "Authorization": "Bearer sk-mag-…" }
    }
  }
}

Both are equivalent once connected — same tools, same account. Catalog tools work with no credential either way; generate / get_generation / wait_for_generation need one. Run the hosted server yourself with:

paying inside the tool call (x402 over MCP)

No key at all? Call generate anyway. Out of credits (or no account), the result is isError: true with the x402 PaymentRequired at the top level (accepts, resource, …) plus error: "payment_required". An x402-capable MCP client — the x402 SDK's x402MCPSession — signs accepts[0] and calls the same tool again with the payment in _meta["x402/payment"]. The server forwards it to the backend as PAYMENT-SIGNATURE; the backend verifies, settles on Base and, for a wallet with no account, creates one. The settled result carries the on-chain receipt in _meta["x402/payment-response"] and x402_receipt. No API key is returned — subsequent requests use wallet-signed auth headers (X-Wallet-Address, X-Wallet-Signature, X-Wallet-Timestamp) instead. The server holds no payment logic; everything is decided by the backend's /api/gens/ contract.

wallet-signed authentication

After the first x402 payment creates the wallet's account, all subsequent requests are authenticated by signing a short message with the wallet's private key. Three headers on every request:

The challenge string is:

Maginary: authenticate <address> at <timestamp>. This does not move funds.

with <address> lowercased and <timestamp> the same unix seconds sent in the header. The timestamp must be within 5 minutes of the server's clock (30 s of future skew tolerated). No API key management needed — the wallet is the credential.

pip install "maginary-mcp[http]"
maginary-mcp-http          # serves /mcp on 0.0.0.0:8642 (MAGINARY_MCP_PORT to change)
# — or —
docker build -t maginary-mcp . && docker run -p 8642:8642 maginary-mcp

The hosted server sets no MAGINARY_API_KEY (keys come per-request). Extra env: MAGINARY_MCP_HOST (default 0.0.0.0), MAGINARY_MCP_PORT (default 8642).

Claude Skill

The server ships an Agent Skill that teaches the --flag DSL, model selection, and the async generate→poll flow:

maginary-mcp --install-skill   # -> ~/.claude/skills/maginary-image-gen/SKILL.md

The skill stands on its own — hosts without MCP get the DSL plus the raw REST calls (POST /gens/ → poll). With the server connected, Claude instead calls search_parameters for the authoritative flag list and generate/wait_for_generation natively. Re-running updates it; local edits are protected unless you pass --force. Source: src/maginary_mcp/SKILL.md.

tools

catalog (no auth)

• list_parameters(category?, status?, include_reserved=false) — enumerate the catalog
• search_parameters(query, category?, include_reserved=false) — text search over names / aliases / desc / examples
• get_parameter(name) — full record for one flag (canonical name or alias)

list_parameters responses include the categories / statuses taxonomy, and both list/search responses carry source (live vs bundled-snapshot).

generation (auth required)

• generate(prompt, callback_url?) — POST /api/gens/. Supports img2img: place image URLs in the prompt. Multiple URLs = multi-input compositing. Use --sref <url> for style-only transfer (not img2img).
• upload_image(file_path, filename?) — reads a local image file and uploads via POST /api/images/upload/. Returns a CDN URL for use in img2img prompts or --sref. Stdio connections only (hosted: use a URL directly or the REST endpoint).
• execute_action(generation_uuid, action_type, parent_image_index?, prompt?, callback_url?) — POST /api/gens/{uuid}/actions/. Run a follow-up on a completed generation's image (upscale, vary, pan, zoom, img2vid, reroll).
• get_generation(uuid) — GET /api/gens/{uuid}/. Response includes processing_result.available_actions mapping slots to valid action types.
• wait_for_generation(uuid, timeout_s=45) — poll to done / failed; a timeout result means still running — call again

worked example

Inside an MCP-capable client, once configured:

"Search the maginary catalog for anything about aspect ratio."

The LLM calls search_parameters("aspect") and gets back the --ar entry with values, examples, and supported models.

"Now generate a cinematic portrait 16:9 with the flagship model."

The LLM calls generate("a cinematic portrait --ar 16:9 --flagship"), gets a uuid, then wait_for_generation(uuid) and reads image_urls[] out of the terminal record.

"Upscale the first image."

The LLM checks processing_result.available_actions["0"], sees "upscale_2x", calls execute_action(uuid, "upscale_2x", 0), gets a new uuid, then wait_for_generation(new_uuid).

"Edit this photo to look like a watercolor." (user provides a local image)

The LLM calls upload_image("/tmp/photo.png") → gets a CDN URL, then generate("https://cdn.maginary.ai/…/photo.webp reimagine as watercolor painting"). (stdio only — on hosted, the user provides a URL instead.)

catalog freshness

• Live fetch on startup from https://maginary.ai/docs/parameters.json, 5-second timeout.
• Bundled snapshot at src/maginary_mcp/parameters_snapshot.json used as a fallback whenever live fetch fails (no network, docs site down, etc.).
• The snapshot is refreshed manually by the maintainer via python scripts/refresh_snapshot.py — deliberately not baked into the wheel build so a new snapshot always corresponds to a reviewed commit.

The source field on list_parameters / search_parameters responses tells you which one is active.

development

cd mcp
python -m venv venv && source venv/bin/activate
pip install -e .
maginary-mcp   # runs on stdio; kill with Ctrl+D

license

MIT.