Content
# gpt-image-2-mcp
An MCP server that exposes OpenAI's **gpt-image-2** (released 2026-04-21) to any MCP client — Claude Desktop, Claude Code, Cursor, MCP Inspector, etc.
Six tools:
| Tool | What it does |
|---|---|
| `generate_image` | text → image |
| `edit_image` | 1–8 reference images (+ optional mask) → image |
| `start_edit_session` | begin an iterative multi-turn edit |
| `continue_edit_session` | apply another refinement turn — previous output becomes the new input |
| `end_edit_session` | release a session |
| `list_edit_sessions` | show active sessions |
Every generated image is **saved to disk** and **returned inline** so the calling model sees it.
## Requirements
- Node.js ≥ 20
- An OpenAI API key on an org with `gpt-image-2` access (Organization Verification may be required)
## Install
```bash
pnpm install
pnpm run build
```
This produces `build/index.js`, which is the server entry point.
## Configure a client
### Claude Desktop
Edit `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows):
```json
{
"mcpServers": {
"gpt-image-2": {
"command": "node",
"args": ["/absolute/path/to/gpt_image_2_mcp/build/index.js"],
"env": {
"OPENAI_API_KEY": "sk-..."
}
}
}
}
```
### Claude Code
Either add to `~/.claude.json` under `mcpServers` with the same shape, or drop an `.mcp.json` next to your project:
```json
{
"mcpServers": {
"gpt-image-2": {
"command": "node",
"args": ["/absolute/path/to/gpt_image_2_mcp/build/index.js"],
"env": { "OPENAI_API_KEY": "sk-..." }
}
}
}
```
### MCP Inspector (interactive testing)
```bash
pnpm run inspect
```
Launches the official inspector UI pointed at your local build.
## Environment variables
| Var | Required | Purpose |
|---|---|---|
| `OPENAI_API_KEY` | ✅ | Auth |
| `OPENAI_BASE_URL` | | Override for proxies / enterprise routes |
| `OPENAI_ORG_ID` | | Forwarded as `organization` |
| `OPENAI_PROJECT_ID` | | Forwarded as `project` |
| `GPT_IMAGE_2_OUTPUT_DIR` | | Global default for where images are saved. Absolute paths used as-is, relative resolved from CWD. |
| `GPT_IMAGE_2_MCP_DEBUG` | | Set to `1` to emit verbose debug logs on stderr. |
| `GPT_IMAGE_2_SESSION_MAX` | | Max concurrent in-memory edit sessions, LRU-evicted beyond this (default 20; `0` = no cap). |
| `GPT_IMAGE_2_SESSION_TTL_MS` | | Idle TTL before an edit session is swept (default 3600000 = 1h; `0` = never expire). |
| `OPENAI_FORCE_RESPONSES_EDITS` | | Set to `1` to pin edits to the Responses-API fallback route instead of `/v1/images/edits`. See **Edit routing** below. |
| `OPENAI_RESPONSES_EDIT_MODEL` | | Host model used by the Responses-API **fallback** edit route (default `gpt-4.1-mini`). See **Edit routing** below. |
## Where images go
Unless overridden, each tool writes to:
```
<OS config dir>/gpt-image-2-mcp/output/<project-name>-<hash>/
```
- macOS/Linux: `~/.config/gpt-image-2-mcp/output/<project>-<hash>/`
- Windows: `%APPDATA%\gpt-image-2-mcp\output\<project>-<hash>\`
`<project>-<hash>` is derived from the git root (if any) or the current working directory — each project gets its own folder so generations don't collide.
**Per-call override:** pass `output_dir: "/some/path"` to any tool.
Filenames look like `image-20260422-150301-a1b2c3.png`. If you pass `filename_prefix: "hero-banner"`, it becomes `image-20260422-150301-a1b2c3-hero-banner.png`.
## What the tools return
Every tool result contains:
1. An inline `ImageContent` block per generated image (so the LLM sees the image)
2. A text summary: applied settings, file path, token usage, estimated cost
3. `structuredContent` for programmatic consumers:
```json
{
"model": "gpt-image-2",
"prompt": "…",
"requested": { "size": "auto", "quality": "auto", "n": 1, "format": "png" },
"applied": { "size": "1024x1024", "quality": "high", "background": "opaque", "output_format": "png" },
"images": [ { "file_path": "…", "filename": "…", "size_bytes": 123456, "mime_type": "image/png" } ],
"usage": { "input_tokens": …, "output_tokens": …, "total_tokens": …, "input_tokens_details": { … } },
"cost_usd_estimated": 0.2112
}
```
Session tools additionally return `session_id` and `turn`.
## Sizes
Default is `auto` (the model picks). You can pass:
- A preset: `1024x1024`, `1536x1024`, `1024x1536`
- Any custom `WxH` where:
- Both edges are multiples of 16
- Max edge ≤ 3840px (outputs above 2K are beta)
- Aspect ratio within 1:3 and 3:1
- Total pixels between 655,360 and 8,294,400
Invalid sizes fail **before** the API call with a clear error — no wasted requests.
**`background: "transparent"` is NOT supported by gpt-image-2.** Use a model that supports it if you need alpha.
## Iterative editing example
```
start_edit_session prompt: "A coastal lighthouse at dawn, photorealistic", images: ["./sketch.png"]
→ session_id: edit-1761149123-a1b2c3d4, turn 1, saved to …/session-…-turn1.png
continue_edit_session session_id: "edit-…-a1b2c3d4", prompt: "Make the sky more orange. Keep everything else the same."
→ turn 2
continue_edit_session session_id: "edit-…-a1b2c3d4", prompt: "Add a small boat on the horizon."
→ turn 3
end_edit_session session_id: "edit-…-a1b2c3d4"
```
Sessions are **in-memory only** and discarded on server restart — this is intentional (keeps the server stateless on the wire) and mirrors the Gemini MCP pattern.
## Image inputs for `edit_image` and `start_edit_session`
Accepts any mix of:
- Absolute path: `/Users/me/photo.png`
- Relative path: `./photo.png` (resolved from CWD)
- `file:///Users/me/photo.png`
- `https://example.com/photo.png` (downloaded, size-capped)
- `data:image/png;base64,iVBOR…`
Up to 8 images per call. Each ≤ 50MB. PNG/WEBP/JPG supported.
## Cost guardrails
The server ships **no hard spending limits** — you should watch your OpenAI usage dashboard. Each tool result includes an estimated cost in USD computed from the token usage returned by the API, plus an approximate pre-flight estimate logged to stderr.
Rough per-image cost at common sizes:
| Quality | 1024×1024 | 1024×1536 / 1536×1024 |
|---|---|---|
| low | ~$0.006 | ~$0.005 |
| medium | ~$0.053 | ~$0.041 |
| high | ~$0.211 | ~$0.165 |
Custom sizes scale with pixel count. Edit calls additionally tokenize input images at high fidelity — large reference images are expensive.
## Edit routing
`edit_image`, `start_edit_session`, and `continue_edit_session` call `POST /v1/images/edits` directly. This is the canonical endpoint: it supports `n > 1`, masks, and returns accurate per-call token usage for cost estimation.
> **History:** at launch (2026-04-21) the endpoint rejected `gpt-image-2` (and `gpt-image-1.5`) with `400 Invalid value: 'gpt-image-2'. Value must be 'dall-e-2'.` — an OpenAI-side bug. Versions ≤ 0.2.0 of this server therefore routed edits through the Responses API by default. OpenAI fixed the endpoint silently in early May 2026 (verified live 2026-06-11), and since 0.3.0 the direct endpoint is the default again.
The Responses-API workaround is kept as a **fallback** (`src/utils/edit-via-responses.ts`):
- It engages automatically if the direct endpoint ever returns the launch-era 400 again (matched narrowly; the rejection is remembered for 10 minutes so only the first call in that window pays the failed attempt, then the direct endpoint is re-probed).
- Set `OPENAI_FORCE_RESPONSES_EDITS=1` to pin it explicitly.
- The legacy `OPENAI_USE_DIRECT_EDITS` toggle from 0.2.0 is deprecated and ignored (its only meaningful setting was `1` — opt into the direct endpoint, which is now the default).
Fallback mechanics: input images are uploaded via the Files API (`purpose: "vision"`), a cheap host model (default `gpt-4.1-mini`, override with `OPENAI_RESPONSES_EDIT_MODEL`) is forced to invoke the `image_generation` tool, the base64 result is extracted, and uploaded files are deleted afterwards.
**Fallback trade-offs versus the direct endpoint** (only apply when the fallback is active — the tool result carries `route: "responses"` and a note when they do):
- `n > 1` is not supported — the Responses path returns one image per call.
- Cost accounting undercounts — `usage` only reports the host chat model's text tokens; the image tool is billed separately (~$0.04–0.05 extra for a 1024×1536 medium edit).
- Masks still work — uploaded and referenced via `input_image_mask.file_id`.
## Troubleshooting
- **"OPENAI_API_KEY is not set"** — add it to the `env` block of your MCP config.
- **`403 / organization verification`** — gpt-image-2 may require Organization Verification on your OpenAI org. Check the dashboard.
- **`429`** — you hit the IPM (images per minute) cap for your tier. Lower `n`, or wait.
- **Image doesn't appear in the client** — check the file path in the text block; the image is saved regardless of inline display.
- **Protocol disconnects silently** — something printed to stdout. Check `src/**/*.ts` — all logs must use `utils/logger.ts` (stderr). This is the single biggest MCP footgun.
## Development
```bash
pnpm run dev # tsx watch
pnpm run typecheck # tsc --noEmit
pnpm run build # compile to build/
pnpm run inspect # launch MCP Inspector
```
## License
MIT
