How to Use Mochify via MCP: AI Agent Image Compression (2026)

What is the Mochify MCP server?

MCP (Model Context Protocol) is a standard that lets AI assistants call external tools and services mid-conversation. We've built a Mochify MCP server that exposes our squish engine as a callable tool — meaning any AI assistant with MCP support can compress, resize, and optimize your images on your behalf.

This is the agent-first version of Mochify. The same compression quality and zero-retention privacy you get from the web tool at mochify.xyz, accessed directly from your AI workflow without opening a browser.

How the MCP connection works

When you describe an image optimization task to your MCP-connected assistant, it parses your prompt and identifies the compression intent. It then calls the Mochify MCP server with the appropriate flags — type, resize dimensions, quality, EXIF handling — and returns the optimized file back into your workflow.

The flow looks like this:

1. You describe the task in natural language to your AI assistant
2. The assistant identifies it as an image compression job
3. It calls the Mochify MCP tool with the right parameters
4. Our squish engine processes the image in memory
5. The optimized file is returned — no upload portal, no manual steps

The underlying engine is the same native C++ squish pipeline that powers the web tool. You're getting identical compression quality, just accessed from inside an AI agent context. (web.dev has a solid primer on why image optimization matters for Core Web Vitals if you want background on why this matters at scale.)

What the LLM controls: type, size, EXIF

The AI assistant connected via MCP makes the same decisions as the natural language feature on the web tool — but in an agentic, automated context. Here's exactly what it can set:

How to connect Mochify via MCP

The Mochify CLI includes the MCP server. Install it, then point your client at it.

1. Install the CLI

macOS (Homebrew):

brew tap getmochify/mochify
brew install mochify

Linux / WSL:

# x86_64 (most Linux / WSL)
curl -L https://github.com/getmochify/mochify-cli/releases/latest/download/mochify-linux-x86_64 -o mochify
chmod +x mochify && sudo mv mochify /usr/local/bin/

# arm64 (Raspberry Pi, AWS Graviton, etc.)
curl -L https://github.com/getmochify/mochify-cli/releases/latest/download/mochify-linux-arm64 -o mochify
chmod +x mochify && sudo mv mochify /usr/local/bin/

All binaries are statically linked — no dependencies needed. Also available on the GitHub releases page.

2. Add to your MCP client config

For Claude Desktop, edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "mochify": {
      "command": "mochify",
      "args": ["serve"]
    }
  }
}

If you have an API key (for higher limits), pass it via the environment:

{
  "mcpServers": {
    "mochify": {
      "command": "mochify",
      "args": ["serve"],
      "env": { "MOCHIFY_API_KEY": "your-key" }
    }
  }
}

Restart Claude Desktop after saving. The Mochify squish tool will appear in your assistant's available functions — test it with: "Compress this image as a WebP for the web."

Example: compressing images from a prompt

Say you're working in Claude Desktop and you say:

"Can you compress this product photo as an Instagram-ready WebP and strip the EXIF data?"

Behind the scenes, the assistant calls Mochify's MCP server with roughly:

• --type webp
• --resize 1080x1080
• --strip-exif

The result comes back in seconds — a web-optimized 1080×1080 WebP with no metadata attached. You didn't touch a settings panel or look up Instagram's recommended dimensions. The agent handled the full decision chain.

MCP vs. the web tool: when to use each

Both options use the same squish engine and deliver the same output quality. The difference is workflow fit.

If you're optimizing images as part of a larger agent workflow — content pipelines, automated publishing, code-adjacent tasks — MCP is the right path. For quick, individual jobs, the web tool is faster to reach.

Cheat sheet: prompt-to-flag mapping

FAQ

Related guides

• How to Self-Host an Image Optimization API with Docker — Full control, local deployment
• The 2026 Guide to Next-Gen Formats: WebP, AVIF, and JPEG XL — Format benchmarks and use cases
• Privacy & Image Optimization: A Comprehensive Guide (2026) — Zero-retention explained in depth
• The Risks of EXIF Data in Image Compression (2026) — Why metadata stripping matters