Image Tools - Background Removal, Upscaling & Face Restoration MCP Server

by fasuizu-br Your server? Claim it

answering

Image Tools - Background Removal, Upscaling & Face Restoration is answering right now. Last checked 14 min ago. It exposes 4 tools. Last commit 3 Aug 2026.

Background removal, 4x upscaling, and face restoration via GPU

Uptime history 48 days of history · worst day 80%

48 days agonow

90.1%

Uptime 24h

82 of 91 checks

Tools

read from the server

987 ms

Response time

average over 24h

Stars

last commit 3 Aug 2026

Image Tools - Background Removal, Upscaling & Face Restoration does not always answer

Over the last week it answered 90.9% of our checks. We check every 15 minutes, so you hear about the next outage within the hour — not from your users.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 14 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add image-tools --transport http https://apim-ai-apis.azure-api.net/mcp/image/mcp

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "image-tools": {
      "url": "https://apim-ai-apis.azure-api.net/mcp/image/mcp"
    }
  }
}

~/.codex/config.toml

[mcp_servers.image-tools]
url = "https://apim-ai-apis.azure-api.net/mcp/image/mcp"

.cursor/mcp.json

{
  "mcpServers": {
    "image-tools": {
      "url": "https://apim-ai-apis.azure-api.net/mcp/image/mcp"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "image-tools": {
      "url": "https://apim-ai-apis.azure-api.net/mcp/image/mcp"
    }
  }
}

Available tools 4

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

background

remove_background

Remove the background from an image. Uses BiRefNet segmentation to precisely separate foreground from background. Returns a base64-encoded image with transparent background (PNG) or white background (WebP). Sub-500ms latency on GPU. Args: image_base64: Base64-encoded image data (PNG, JPEG, or WebP). output_format: Output format -- 'png' (with transparency) or 'webp'. Returns: dict with keys: - image_base64 (str): Base64-encoded result image - format (str): Output image format - original_size (dict): Original width and height - processing_ms (int): Processing time in milliseconds

image

check_image_service

Check health status of Image API services and loaded models. Returns: dict with keys: - status (str): 'healthy' or error state - models (dict): Loaded model status per capability - version (str): API version

restore

restore_face

Restore and enhance faces in an image using GFPGAN. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background using Real-ESRGAN. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP). upscale: Output upscale factor -- 1 to 4 (default: 2). enhance_background: Whether to enhance background with Real-ESRGAN (default: true). Returns: dict with keys: - image (str): Base64-encoded restored image - format (str): Output image format - width (int): Output width - height (int): Output height - upscale (int): Scale factor applied - processing_time_ms (float): Processing time in milliseconds

upscale

upscale_image

Upscale image resolution using Real-ESRGAN. Enhances image resolution by 2x or 4x using GPU-accelerated Real-ESRGAN super-resolution. Processes in tiles (256x256) to manage VRAM. Maximum output dimension: 8192x8192. Args: image_base64: Base64-encoded image data (PNG, JPEG, or WebP). scale: Upscale factor -- 2 or 4 (default: 4). Returns: dict with keys: - image (str): Base64-encoded upscaled image - format (str): Output image format - width (int): Output width - height (int): Output height - scale (int): Scale factor applied - processing_time_ms (float): Processing time in milliseconds

Endpoints

URL	Transport	State	Latency	Checked
https://apim-ai-apis.azure-api.net/mcp/image/mcp	streamable-http	answering	710 ms	14 min ago

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Image Tools - Background Removal, Upscaling & Face Restoration — questions

Answers built from our own checks of this server.

What can Image Tools - Background Removal, Upscaling & Face Restoration do?

It exposes 4 tools, read directly from the server on our last check. Among them: check_image_service, remove_background, restore_face, upscale_image. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

Is Image Tools - Background Removal, Upscaling & Face Restoration working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 82 of 91 checks got a reply (90.1%), average response time 987 ms. The bar chart above shows every period we have measured.

How do I connect Image Tools - Background Removal, Upscaling & Face Restoration?

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.

Does Image Tools - Background Removal, Upscaling & Face Restoration need an API key?

No. Image Tools - Background Removal, Upscaling & Face Restoration completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 4 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is Image Tools - Background Removal, Upscaling & Face Restoration?

It answers our handshake in 987 ms on average, which is faster than 9% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.

Is Image Tools - Background Removal, Upscaling & Face Restoration open source?

Yes — it is published under the MIT licence, written in JavaScript, 2 stars on GitHub and 24 open issues. The source link is on this page, so you can read exactly what it does with your data before you connect it.