exif-mcp

An MCP server that allows LLMs (or humans) to read image metadata on-demand, entirely offline. Based on the excellent exifr library it's exremely fast and does not rely on any external tools.

Usecases:

• Analyze image metadata and visualize it

• Perform analysis of your image library: what are my most used cameras? Lens distribution? Which dates of the week I take most pictures on? Most favorite locations?

• Debugging image manipulation code.

Ths tool is used extensively by the reverse geolocation service PlaceSpotter for development and testing.

Overview

exif-mcp is a Model Context Protocol (MCP) server that provides tools for extracting various metadata segments from images. Built with TypeScript, it leverages the excellent exifr library to parse metadata from images in common formats like JPEG, PNG, TIFF, and HEIC. This allows this service to parse image metadata without executing any external tools which allows it to be both highly efficient and secure.

Features

• Local operation: Works completely offline with no remote network required

• Multiple segments: Extracts EXIF, GPS, XMP, ICC, IPTC, JFIF, and IHDR metadata

• Various input formats: Supports JPEG, TIFF, HEIC/AVIF, and PNG

• Flexible image sources: Read from file system, URLs, base64 data, or buffers

• Specialized tools: Get orientation, rotation info, GPS coordinates, and thumbnails

Related MCP server: llm-vision-mcp

Installation

# Clone the repository
git clone https://github.com/stass/exif-mcp.git
cd exif-mcp

# Install dependencies
npm install

# Build the project
npm run build

Usage

Claude Desktop

Put this into Claude config file (claude_desktop_config.json):

"mcpServers": {
    "exif-mcp": {
      "command": "node",
      "args": [
        "/path/to/exif-mcp/dist/server.js"
      ]
    }
  },

Restart Claude. Now you can ask Claude to inspect images for you or e.g. find files taken with specific camera. This works best in combination with filesystem MCP tools so Claude can find files and list directories.

Starting the server

# Start the server
npm start

# For development with auto-reload
npm run dev

The server uses the StdioServerTransport from the MCP SDK, making it compatible with any MCP client that supports STDIO transport.

You can use mcp-proxy to enable remote access.

Available Tools

The following tools are provided by the server:

Web UI (browser)

A built-in web interface — the Metadata Lab — lets you use every feature from any browser without an MCP client:

npm run build
npm run web
# open http://localhost:3000

Add WEB_OPEN=1 to auto-open the browser, or change the port with WEB_PORT.

Analyze — drag & drop one or many images (or paste a URL) for instant metadata:

• Summary cards (camera, lens, exposure, orientation), a full tag table, and raw JSON export

• GPS coordinates with OpenStreetMap / Google Maps links and a one-click copy

• Batch mode: every dropped image gets a thumbnail in a filmstrip you can switch between

Edit EXIF — edit camera fields (Make, Model, Software, dates, copyright…), set GPS coordinates, or clear GPS, then download a modified copy. The original file is never touched.

Clean — remove all metadata (EXIF, GPS, XMP, ICC, IPTC) from JPEG/PNG with one click. Lossless: only metadata segments are stripped, pixels are untouched.

The web server exposes a JSON API as well:

curl -X POST http://localhost:3000/api/analyze \
  -H 'Content-Type: application/json' \
  -d '{"image":{"kind":"path","path":"/path/to/photo.jpg"}}'

curl -X POST http://localhost:3000/api/strip \
  -H 'Content-Type: application/json' \
  -d '{"image":{"kind":"path","path":"/path/to/photo.jpg"}}'

curl -X POST http://localhost:3000/api/edit \
  -H 'Content-Type: application/json' \
  -d '{"image":{"kind":"base64","data":"..."},"fields":{"Make":"My Camera"},"gps":{"latitude":40.71,"longitude":-74.0}}'

Image sources match the MCP tool format (path, url, base64, buffer).

Debugging with MCP Inspector

1. Start the inspector: npx @modelcontextprotocol/inspector node dist/server.js

2. Connect to it with MCP Inspector using the STDIO transport

3. Call a tool, e.g., read-metadata with parameter:

   {
     "image": {
       "kind": "path",
       "path": "/path/to/image.jpg"
     }
   }

4. You cal also use MCP inspector command line like this: npx @modelcontextprotocol/inspector --cli node dist/server.js --method tools/call --tool-name read-exif --tool-arg image='{"kind": "path", "path": "/path/to/image.jpeg"}' --tool-arg pick="[]"

Image Source Types

The server supports multiple ways to provide image data:

// From local file system
{
  "kind": "path",
  "path": "/path/to/image.jpg"
}

// From URL (http, https, or file://)
{
  "kind": "url",
  "url": "https://example.com/image.jpg"
}

// From base64 data (raw or data URI)
{
  "kind": "base64",
  "data": "data:image/jpeg;base64,/9j/4AAQSkZ..."
}

// From base64 buffer
{
  "kind": "buffer",
  "buffer": "/9j/4AAQSkZ..."
}

Development

Running Tests

# Run tests
npm test

# Run tests with watch mode
npm run test:watch

Project Structure

exif-mcp/
├── src/
│   ├── server.ts         # Main MCP entry point
│   ├── tools/
│   │   ├── index.ts      # Tool registration
│   │   ├── loaders.ts    # Image loading utilities
│   │   └── segments.ts   # exifr options builders
│   ├── web/
│   │   ├── server.ts     # Browser web server (API + static UI)
│   │   ├── image.ts      # Format detection, JPEG/PNG metadata stripping
│   │   └── edit.ts       # EXIF/GPS editing (piexifjs)
│   └── types/
│       └── image.ts      # Type definitions
├── web/
│   └── index.html        # Metadata Lab browser UI
├── tests/                # Test files
└── README.md

Error Handling

The server provides standardized error handling for common issues:

• Unsupported formats or missing metadata

• Network fetch failures

• Oversized payloads

• Internal exifr errors

License

BSD 2-clause

Acknowledgements

• exifr - Extremely fast and robust EXIF parsing library