TryOnAnytime

npx add-mcp 'https://tryonanytime.com/api/mcp'

Let your AI agent try clothes on for you.

One MCP endpoint and any MCP-capable AI client can call our virtual try-on engine directly — send a photo of a person and a garment, and the result comes back right in the conversation.

{
  "mcpServers": {
    "tryonanytime": {
      "type": "http",
      "url": "https://tryonanytime.com/api/mcp",
      "headers": { "Authorization": "Bearer sk_your_key" }
    }
  }
}

What is MCP?

Model Context Protocol (MCP) is an open standard that lets AI assistants call external tools. TryOnAnytime exposes its virtual try-on engine as an MCP server, so your agent can dress your photos without you ever leaving the chat.

Same engine as the REST API

MCP is a protocol layer on top of the same try-on pipeline — identical quality, identical credits. Use REST when you script; use MCP when your agent does the talking.

Read the REST API reference

Connect in one minute

Point your MCP client at a single HTTP endpoint and authenticate with your API key.

Config JSON

Paste this into your MCP client's config file (project or global, whichever it supports) and restart it.

Any Streamable HTTP client

The server speaks MCP over Streamable HTTP. Point any compliant client at the URL with the Bearer header.

Legacy clients (mcp-remote)

Only speak stdio? Bridge the endpoint with mcp-remote — snippet below.

{
  "mcpServers": {
    "tryonanytime": {
      "type": "http",
      "url": "https://tryonanytime.com/api/mcp",
      "headers": { "Authorization": "Bearer sk_your_key" }
    }
  }
}

Tools reference

Three tools. That's the whole surface.

virtual_tryon

Submit a virtual try-on. When the render finishes within the call, the result image is returned directly as MCP image content.

virtual_tryon({
  "person_url": "https://…/me.jpg",       // required
  "garment_url": "https://…/jacket.jpg",  // required
  "prompt": "keep my face and pose"       // optional
})

get_tryon_result

Poll a task's status and fetch the result image once it succeeds.

get_tryon_result({
  "task_id": "a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d"
})

get_credit_balance

Check the credit balance on the current API key.

get_credit_balance()
// no arguments

See it in a conversation

No new UI, no tab switching — just ask.

Your AI agent+ tryonanytime MCP

Put this jacket on my photo

virtual_tryon(person_url, garment_url)

Done — here's you in the jacket. This try-on used 10 credits; you have 32 left.

tryon-result.png

Billing & limits

10 credits per try-on

Every virtual_tryon call deducts 10 credits from your account — same price as the REST API. Top up or subscribe any time.

Rate limit

virtual_tryon: one call every 5 seconds per key owner (the account the key belongs to). Reads (get_tryon_result, get_credit_balance): 500 ms per key owner, 1 second per IP. Agents should poll get_tryon_result instead of spamming virtual_tryon.

Key management

Create and revoke keys in your dashboard. One key works across MCP, the REST API, and the browser extension.

Keep building

REST API

The same try-on engine over plain HTTP — endpoints, errors, and code samples.

Chrome Extension

Right-click any product photo on the web and try it on yourself.

Embed widget

Add a try-on button to your own storefront with a publishable key.

Give your agent try-on superpowers

One MCP endpoint, three tools, the same credits as the REST API. Connect your client and let your agent dress the photos.