gpt-image-mcp

An MCP server that lets coding agents draw. It wraps an OpenAI-compatible /v1/images API and exposes it as two tools, so pi, Claude Code, Cursor, Codex, and anything else that speaks MCP can generate and edit images as part of a normal task.

Text-only chat models cannot produce images, and most agent harnesses have no image tool. This fills that gap: the model calls generate_image, gets the picture back inline (so it can actually look at what it made) and a file on disk (so the next turn can reference, move, or ship it).

• Zero dependencies — one server.mjs, Node built-ins only

• Stdio transport, 2025-06-18 protocol (negotiates down to 2024-11-05)

• Returns both image content blocks and saved files

• Never touches your key beyond the Authorization header

Tools

Both save every result to disk (./generated-images by default), print the paths, and attach the images inline. Multiple results get -1, -2 suffixes; name collisions are avoided automatically. b64_json and url responses are both handled, so gpt-image and dall-e style providers both work.

Related MCP server: DreamLayer image tools

Quick start

# try it without installing (from this repo)
npx -y github:TypeDreamMoon/gpt-image-mcp --check

# or install globally
npm i -g github:TypeDreamMoon/gpt-image-mcp

--check prints the resolved configuration and probes the endpoint. It costs nothing.

export IMAGE_API_KEY=sk-...
node server.mjs --draw "a red fox in the snow, watercolor"   # costs one image

Configure

Everything is an environment variable; there are no config files.

Client setup

pi

~/.pi/agent/mcp.json:

{
  "mcpServers": {
    "gpt-image": {
      "command": "node",
      "args": ["/absolute/path/to/gpt-image-mcp/server.mjs"],
      "timeout": 300,
      "exposure": "direct",
      "env": { "IMAGE_API_KEY": "sk-...", "IMAGE_MODEL": "gpt-image-1" }
    }
  }
}

• timeout: 300 — pi defaults to 60 seconds per request, which image generation will exceed.

• exposure: "direct" — declares the tools to the model up front. Use "deferred" to keep the tool list small behind tool_search, or omit it (default codemode) to reach the tools from scripts.

Check it: pi mcp list should report gpt-image: connected, 2 tools.

Claude Code

claude mcp add gpt-image --scope user \
  --env IMAGE_API_KEY=sk-... --env IMAGE_MODEL=gpt-image-1 \
  -- node /absolute/path/to/gpt-image-mcp/server.mjs
claude mcp list

Cursor, Claude Desktop, Codex, anything else

Use the standard mcpServers entry — command, args, env — wherever that client keeps its MCP configuration. Codex takes the TOML equivalent under [mcp_servers.gpt-image].

Verify without spending money

node test.mjs

Runs a local mock of the OpenAI image API and drives the server over real stdio JSON-RPC: handshake, tools/list, schema shape, generation, multipart editing, file saving, output_dir override, usage passthrough, missing-key errors, provider 401s, and unknown tools. 18 checks, no network, no key.

Provider requirements

The server needs an endpoint that implements the OpenAI images API:

POST {IMAGE_BASE_URL}/images/generations   ← generate_image
POST {IMAGE_BASE_URL}/images/edits         ← edit_image (multipart)

Official OpenAI (gpt-image-1, gpt-image-1-mini, dall-e) works out of the box. Many OpenAI-compatible relays also expose /v1/images/generations; point IMAGE_BASE_URL at theirs and set IMAGE_MODEL to the name they list.

⚠️ Plenty of chat-only relays advertise image models in /v1/models but return 404 on the images endpoints, and some reject image_generation inside /v1/responses. Run --check and then --draw before wiring it into an agent. A relay that only serves chat completions cannot be used here.

Notes and limitations

• Images are returned inline by default. A 1024×1024 PNG is roughly 1–3 MB of base64, which costs context on every turn it stays in history; set IMAGE_INLINE=0 to keep files only.

• No retries. Failures here are usually quota or moderation, which retrying will not fix — the provider's message is passed through verbatim.

• Generated images bypass any image downscaling the harness applies to files it reads.

• edit_image sends multipart/form-data; a few relays expect a JSON body with images[].image_url instead. That shape is not supported yet.

License

MIT