apimodels-mcp

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.

Fastest setup: the Codex / Claude Code plugin

Coding agents are good at planning — scripts, shot lists, prompts, editing with ffmpeg. The apimodels plugin gives them the part they cannot do: rendering the video, images and voice-over. It bundles this MCP server plus a skill that teaches the agent when to use it, how to pick a model, how to wait for long renders and when to ask before spending.

1. Sign in once (needs Node.js 18+):
   bashCopy

   npx -y apimodels-mcp login
   

   Your browser opens apimodels.app; click Authorize. A key named CLI · <date> is created for this machine and stored in ~/.apimodels/credentials.json, where every client finds it — including desktop apps that do not see your shell's environment variables. Nothing to copy or paste, and the agent can run this step for you. On a machine without a browser use --no-browser (prints the link) or --paste (paste a key from https://apimodels.app/console/api-keys).
2. Install the plugin.
   • Claude Code (2.1.275 or later):
     Copy

     /plugin install apimodels --marketplace stimQQ/apimodels-mcp
     

     Older versions: /plugin marketplace add stimQQ/apimodels-mcp, then /plugin install apimodels@apimodels.
   • Codex:
     bashCopy

     codex plugin marketplace add stimQQ/apimodels-mcp
     codex plugin add apimodels@apimodels
     

     Only have the Codex desktop app and no codex command? Use npx -y @openai/codex in place of codex — it runs the official CLI on the fly and shares the app's config. Then restart Codex. Codex asks you to approve each call that spends credits (generate_video, generate_image, text_to_speech, chat, review_image); checking progress, the model list and the balance never asks.
3. Ask in plain words, e.g. "make a 5-second 16:9 video of a kitten slowly raising its head in morning light" or "plan a 20-second product video for this repo and render it shot by shot".

No Node.js yet? Install the plugin anyway and ask for a video: the skill has the agent check for Node.js and, with your OK, install the official LTS for you (winget on Windows, Apple's installer on macOS).

Hosted server: no Node.js, sign in with OAuth

The same tools run at https://api.apimodels.app/mcp (Streamable HTTP). Nothing to install and no key to copy: add the URL to your client and it asks you to sign in to apimodels.app and click Authorize. The access token it gets is a dedicated API key named MCP · <app> · <date>; delete that key in the console to disconnect.

• Codex — add to ~/.codex/config.toml, then run codex mcp login apimodels:
  tomlCopy

  [mcp_servers.apimodels]
  url = "https://api.apimodels.app/mcp"
  

• Claude Code — claude mcp add --transport http apimodels https://api.apimodels.app/mcp, then /mcp → apimodels → Authenticate.
• ChatGPT, Claude.ai, Cursor and other clients that take a remote MCP URL — add it as a custom connector with this URL and sign in when asked.

Differences from the local server: the hosted one cannot read files on your machine — the agent calls get_upload_url, uploads the file with curl -T and passes the returned URL instead (local paths are refused with that hint) — and each call waits at most 50 seconds before handing back a task id for get_task. Signed in with a plain API key instead? Send it as Authorization: Bearer sk_….

Tools

Long generations do not get lost

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.

• Codex: keep the default. Or raise tool_timeout_sec for this server in config.toml and pass a larger wait_seconds.
• Claude Code / Claude Desktop / Cursor: no 60-second limit, so 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.

The model can check its own work

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":

1. 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.
2. 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.

Local images just work

image_url on generate_image and generate_video takes any of these:

• a public https://… URL — passed through untouched
• a local file path — /Users/me/photo.png, ./ref.jpg, ~/Pictures/x.webp
• a URL on your own machine — http://127.0.0.1:8000/photo.png, http://localhost:3000/…
• a data:image/png;base64,… URI

The 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.

Setup

1. Run npx -y apimodels-mcp login and click Authorize in the browser — or create a key at https://apimodels.app/console/api-keys (it looks like sk_…) and put it in the server's environment as APIMODELS_API_KEY, as the examples below do.
2. Add the server to your MCP client. npx -y apimodels-mcp status shows which key is in use and your balance.

Claude Desktop

Edit claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "apimodels": {
      "command": "npx",
      "args": ["-y", "apimodels-mcp"],
      "env": {
        "APIMODELS_API_KEY": "sk_your_key_here"
      }
    }
  }
}

Restart Claude Desktop. You can now ask it to "generate an image of …" or "make a 5-second video of …".

Cursor

Settings → MCP → Add new MCP server, or add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "apimodels": {
      "command": "npx",
      "args": ["-y", "apimodels-mcp"],
      "env": { "APIMODELS_API_KEY": "sk_your_key_here" }
    }
  }
}

Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.apimodels]
command = "npx"
args = ["-y", "apimodels-mcp"]
env = { APIMODELS_API_KEY = "sk_your_key_here" }
# Optional. Codex aborts a tool call after 60 s by default; the tools stay under that
# on their own (see "Long generations do not get lost"), so this is only needed if you
# want generate_video to return the URL in one call — then also pass wait_seconds: 600.
# tool_timeout_sec = 660

Two things to know in Codex:

• Approvals. Codex asks you to approve any tool call that is not marked read-only. Since 0.4.3, list_models and get_task are marked read-only, so waiting on a video no longer prompts on every check; the tools that bill your account (generate_video, generate_image, text_to_speech, chat, review_image) still ask once per call. To skip those prompts as well, add default_tools_approval_mode = "approve" under [mcp_servers.apimodels].
• Name the tool. Codex loads MCP tools on demand rather than listing them all up front, so say which one you want — e.g. "use apimodels generate_video to make a 5-second 16:9 clip of …". A bare "make me a video" may not make it look.

Cherry Studio

In Settings → MCP Servers, add a new server of type stdio:

• Command: npx
• Arguments: -y apimodels-mcp
• Environment variables: APIMODELS_API_KEY=sk_your_key_here

Enable 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.

Models, docs and pricing

Everything the tools call is documented on apimodels.app:

• API documentation · pricing · full model catalog
• Image: GPT Image 2.5 API (model page), GPT Image 2 API, Nano Banana 2.1 API (model page), all image models
• Video: Seedance 2.5 API, Google Veo API, MiniMax H3 API, FlashVSR video upscaling
• Chat and speech: LLM API (GPT, Claude, Gemini, DeepSeek, GLM, Qwen), Claude Sonnet 5.5, audio and text-to-speech
• Other ways in: Claude Code setup, chat clients, Agent Skills, free calculators and tools
• Prompt libraries with example outputs: GPT Image 2.5 prompts, GPT Image 2 prompts, Seedance 2.5 prompts, MiniMax H3 prompts

Configuration

Local development

pnpm install
pnpm build
APIMODELS_API_KEY=sk_... node dist/index.js   # runs over stdio

License

MIT