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2025-04-13

Un servicio de protocolo de contexto modelo (MCP) para recuperar las dimensiones de imágenes, admitiendo tanto la URL como las fuentes de archivos locales.

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Image Tools MCP

A Model Context Protocol (MCP) service for retrieving image dimensions and compressing images, supporting both URL and local file sources.

中文文档

Features

  • Retrieve image dimensions from URLs
  • Get image dimensions from local files
  • Compress images from URLs using TinyPNG API
  • Compress local images using TinyPNG API
  • Convert images to different formats (webp, jpeg/jpg, png)
  • Returns width, height, type, MIME type, and compression information

Example Results

Example Result 1 Example Result 2

download from figma url and compress Example Result 3

Usage

Using as an MCP Service

This service provides five tool functions:

  1. get_image_size - Get dimensions of remote images
  2. get_local_image_size - Get dimensions of local images
  3. compress_image_from_url - Compress remote images using TinyPNG API
  4. compress_local_image - Compress local images using TinyPNG API
  5. figma - Fetch image links from Figma API and compress them using TinyPNG API

Client Integration

To use this MCP service, you need to connect to it from an MCP client. Here are examples of how to integrate with different clients:

Using with Claude Desktop

  1. Install Claude Desktop from claude.ai/download
  2. Get TinyPNG API key: Visit TinyPNG and get your API key
  3. Configure Claude Desktop to use this MCP server by editing the configuration file:
{
  "mcpServers": {
    "image-tools": {
      "command": "npx",
      "args": ["image-tools-mcp"],
      "env": {
        "TINIFY_API_KEY": "<YOUR_TINIFY_API_KEY>",
        "FIGMA_API_TOKEN": "<YOUR_FIGMA_API_TOKEN>"
      }
    }
  }
}
  1. Restart Claude Desktop
  2. Ask Claude to get image dimensions: "Can you tell me the dimensions of this image: https://example.com/image.jpg"
  3. Ask Claude to compress an image: "Can you compress this image: https://example.com/image.jpg"
  4. Ask Claude to compress a local image: "Can you compress this image: D:/path/to/image.png"
  5. Ask Claude to compress a local image folder: "Can you compress this folder: D:/imageFolder"
  6. Ask Claude to fetch image links from Figma API: "Can you fetch image links from Figma API: https://www.figma.com/file/XXXXXXX"

Using with MCP Client Library

import { McpClient } from "@modelcontextprotocol/client";

// Initialize the client
const client = new McpClient({
  transport: "stdio" // or other transport options
});

// Connect to the server
await client.connect();

// Get image dimensions from URL
const urlResult = await client.callTool("get_image_size", {
  options: {
    imageUrl: "https://example.com/image.jpg"
  }
});
console.log(JSON.parse(urlResult.content[0].text));
// Output: { width: 800, height: 600, type: "jpg", mime: "image/jpeg" }

// Get image dimensions from local file
const localResult = await client.callTool("get_local_image_size", {
  options: {
    imagePath: "D:/path/to/image.png"
  }
});
console.log(JSON.parse(localResult.content[0].text));
// Output: { width: 1024, height: 768, type: "png", mime: "image/png", path: "D:/path/to/image.png" }

// Compress image from URL
const compressUrlResult = await client.callTool("compress_image_from_url", {
  options: {
    imageUrl: "https://example.com/image.jpg",
    outputFormat: "webp" // Optional: convert to webp, jpeg/jpg, or png
  }
});
console.log(JSON.parse(compressUrlResult.content[0].text));
// Output: { originalSize: 102400, compressedSize: 51200, compressionRatio: "50.00%", tempFilePath: "/tmp/compressed_1615456789.webp", format: "webp" }

// Compress local image
const compressLocalResult = await client.callTool("compress_local_image", {
  options: {
    imagePath: "D:/path/to/image.png",
    outputPath: "D:/path/to/compressed.webp", // Optional
    outputFormat: "image/webp" // Optional: convert to image/webp, image/jpeg, or image/png
  }
});
console.log(JSON.parse(compressLocalResult.content[0].text));
// Output: { originalSize: 102400, compressedSize: 51200, compressionRatio: "50.00%", outputPath: "D:/path/to/compressed.webp", format: "webp" }

// Fetch image links from Figma API

const figmaResult = await client.callTool("figma", {
  options: {
    figmaUrl: "https://www.figma.com/file/XXXXXXX"
  }
});
console.log(JSON.parse(figmaResult.content[0].text));
// Output: { imageLinks: ["https://example.com/image1.jpg", "https://example.com/image2.jpg"] }

### Tool Schemas

#### get_image_size

```typescript
{
  options: {
    imageUrl: string // URL of the image to retrieve dimensions for
  }
}

get_local_image_size

{
  options: {
    imagePath: string // Absolute path to the local image file
  }
}

compress_image_from_url

{
  options: {
    imageUrl: string // URL of the image to compress
    outputFormat?: "image/webp" | "image/jpeg" | "image/jpg" | "image/png" // Optional output format
  }
}

compress_local_image

{
  options: {
    imagePath: string // Absolute path to the local image file
    outputPath?: string // Optional absolute path for the compressed output image
    outputFormat?: "image/webp" | "image/jpeg" | "image/jpg" | "image/png" // Optional output format
  }
}

figma

{
  options: {
    figmaUrl: string // URL of the Figma file to fetch image links from
  }
}

Technical Implementation

This project is built on the following libraries:

Environment Variables

  • TINIFY_API_KEY - Required for image compression functionality. Get your API key from TinyPNG
    • When not provided, the compression tools (compress_image_from_url and compress_local_image) will not be registered
  • FIGMA_API_TOKEN - Required for fetching image links from Figma API. Get your API token from Figma
    • When not provided, the Figma tool (figma) will not be registered

Note: The basic image dimension tools (get_image_size and get_local_image_size) are always available regardless of API keys.

License

MIT

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    Reviews

    4 (1)
    Avatar
    user_WN8sZJwi
    2025-04-18

    As a dedicated user of image-tools-mcp, I find it an essential tool for managing and processing images efficiently. The intuitive design and powerful features make it a standout in its category. Kudos to kshern for developing such a robust application. Highly recommend checking it out for your image processing needs!