The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Apimodels listing page.
MCP server for apimodels.app — call image, video, LLM chat and text-to-speech models with one API key, from Claude Desktop, Cursor, or any MCP client.
One key unlocks GPT-5.5, Claude, Gemini, GLM, DeepSeek, Qwen, Seedance, Veo, Kling, gpt-image-2, Gemini Image, MiniMax speech and more — billed in USD, you only pay for successful generations.
| Tool | What it does |
|---|---|
list_models | List available model ids (chat / image / video / audio). |
chat | Chat / text completion with any LLM (gpt-5-5, claude-opus-4-8, gemini-3-pro-preview, …). |
generate_image | Text-to-image or image edit; returns the image URL(s) plus a downscaled preview the model can look at. With doubao-seedream-5-0-flash it can also keep a transparent background (background: "transparent") or split one image into a base plus up to 16 transparent layers with names and positions (layer_decomposition: true, billed per output image). |
review_image | A vision model critiques an image against your brief and proposes a revised prompt. |
generate_video | Text-to-video (optional reference image); returns the video URL(s), or a task id if it is not done within wait_seconds. |
get_task | Wait for / check on a task that generate_image, generate_video or text_to_speech handed back as still running. |
text_to_speech | Text-to-speech (MiniMax voices); returns the audio URL. ElevenLabs TTS is not exposed here — it streams raw bytes from POST /v1/tts/stream rather than returning a URL. |
Every generation is asynchronous on apimodels, and video is slow: a median of about 2.5 minutes, 9 in 10 within 8 minutes (production, week to 2026-09-22). Images take about 50 seconds. Meanwhile Codex aborts an MCP tool call after 60 seconds by default, and the MCP SDK's own client timeout is 60 seconds too. A tool that blocks until the video is ready therefore gets killed mid-wait — the task keeps running, the account is billed when it finishes, and the assistant never sees the URL. Versions up to 0.2.x did exactly that.
Since 0.3.0 the generation tools wait at most wait_seconds (default 50) and then return the task id with a "still running" note; the assistant calls get_task, which waits up to another wait_seconds and returns the URL(s) — with the image preview for image tasks — or "still running" again. Nothing is resubmitted and nothing is billed twice. The assistant does this on its own; you just ask for the video.
tool_timeout_sec for this server in config.toml and pass a larger wait_seconds.wait_seconds: 600 on generate_video gets the URL in one call. APIMODELS_WAIT_SECONDS=600 in the server's env makes that the default.Ask for an image and let the assistant iterate until it is right — "make a 16:9 banner that says SAVE 10%, check the spelling, fix it if needed":
generate_image returns the URL and a preview of the image itself (max 1024px JPEG). Clients that pass tool-result images to the model — Claude Desktop, Claude Code, Cursor — let it see what it made. Pass return_image: false to skip the preview.review_image works everywhere, including clients that show tool-result images to you but not to the model (Cherry Studio is one). It sends the image and your brief to a vision model and returns what matches, what is wrong (garbled text, composition, aspect ratio, artifacts) and a revised prompt. One review costs well under $0.01 on the default gpt-5.6-luna.The assistant picks aspect_ratio and resolution itself from what you ask for, so "make it 16:9" in plain words is enough.
image_url on generate_image and generate_video takes any of these:
https://… URL — passed through untouched/Users/me/photo.png, ./ref.jpg, ~/Pictures/x.webphttp://127.0.0.1:8000/photo.png, http://localhost:3000/…data:image/png;base64,… URIThe last three are uploaded for you first, and the resulting public URL is what gets
generated from. This has to happen here rather than server-side: the file exists only on
your machine, and 127.0.0.1 means our server when our server resolves it — which is why
passing one to the REST API directly fails with private/reserved IP addresses not allowed.
This MCP server runs next to your files, so it can do what our servers cannot.
Uploads land in your account's R2 space and are auto-deleted after 7 days.
sk_…).Edit claude_desktop_config.json (Settings → Developer → Edit Config):
Restart Claude Desktop. You can now ask it to "generate an image of …" or "make a 5-second video of …".
Settings → MCP → Add new MCP server, or add to ~/.cursor/mcp.json:
Add to ~/.codex/config.toml:
In Settings → MCP Servers, add a new server of type stdio:
npx-y apimodels-mcpAPIMODELS_API_KEY=sk_your_key_hereEnable the server, then select it for your conversation from the MCP control under the chat box. Use a chat model that supports tool calls (Claude, GPT, Gemini …) as the conversation model — it calls the image model for you. Cherry Studio needs Node.js installed for npx; on Windows install it from https://nodejs.org.
Any other MCP client works the same way — run npx -y apimodels-mcp over stdio with APIMODELS_API_KEY in the environment.
Everything the tools call is documented on apimodels.app:
| Env var | Default | Description |
|---|---|---|
APIMODELS_API_KEY | — (required) | Your sk_… key. |
APIMODELS_BASE_URL | https://api.apimodels.app/v1 | API base URL. |
APIMODELS_WAIT_SECONDS | 50 | Default wait_seconds for generate_image, generate_video, text_to_speech and get_task: how long a call waits before handing back a task id. Max 900. |
APIMODELS_TIMEOUT_MS | — | Deprecated (0.2.x): the same wait in milliseconds. Still honoured if set. |
MIT