Image Processor

by ia-programming

13 stars

450 downloads

Not rated

GitHub

About

Enables image retrieval, processing, and display from both URLs and local files with automatic compression and formatting for visual content integration.

Details

Author: ia-programming
Repository: IA-Programming/mcp-images
GitHub stars: 13
Downloads: 450
License: MIT License
Categories: Developer Tools, Media, File Management, AI, Design, Infrastructure, Frontend

- Fetch images from URLs (http/https)
- Load images from local file paths
- Specialized handling for large local images
- Automatic image compression for large images (>1MB)
- Parallel processing of multiple images
- Proper MIME type mapping for different file extensions
- Comprehensive error handling and logging

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name Image Processor

Command (node, npx, python, etc.) uv
Arguments
- Argument 1 --directory
- Argument 2 /path/to/mcp-image
- Argument 3 run
- Argument 4 mcp_image.py
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

JSON

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

From the repository

1. Clone this repository
2. Create and activate a virtual environment using uv:
```bash
uv venv

There are two ways to run the MCP server:

`fetch_images`

Fetch and process images from URLs or local file paths. Parameters: image_sources (List of URLs or file paths to images). Returns: List of processed images with base64 encoding and MIME types.

The server provides the following tools:

fetch_images: Fetch and process images from URLs or local file paths

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "image processor": {
            "env": {},
            "args": [
                "--directory",
                "/path/to/mcp-image",
                "run",
                "mcp_image.py"
            ],
            "shell": false,
            "command": "uv"
        }
    }
}

Linux

{
    "env": [],
    "args": [
        "--directory",
        "/path/to/mcp-image",
        "run",
        "mcp_image.py"
    ],
    "shell": false,
    "command": "uv"
}

Macos

{
    "env": [],
    "args": [
        "--directory",
        "/path/to/mcp-image",
        "run",
        "mcp_image.py"
    ],
    "shell": false,
    "command": "uv"
}

Windows

{
    "env": [],
    "args": [
        "--directory",
        "/path/to/mcp-image",
        "run",
        "mcp_image.py"
    ],
    "shell": false,
    "command": "uv"
}

A Model Context Protocol (MCP) server that provides tools for fetching and processing images from URLs, local file paths, and numpy arrays. The server includes a tool called fetch_images that returns images as base64-encoded strings along with their MIME types.

If you find this project helpful and would like to support future projects, consider buying us a coffee! Your support helps us continue building innovative AI solutions.

Your contributions go a long way in fueling our passion for creating intelligent and user-friendly applications.

- Features
- Prerequisites
- Installation
- Running the Server

- Direct Method
- Configure for Windsurf/Cursor

- Python 3.10+
- uv package manager (recommended)
- Clone this repository
- Create and activate a virtual environment using uv:

uv venv # On Windows: .venv\Scripts\activate # On Unix/MacOS: source .venv/bin/activate

There are two ways to run the MCP server:
- Edit the configuration file at ~/.codeium/windsurf/mcp_config.json
- Add the following configuration:

{ "mcpServers": { "image": { "command": "uv", "args": ["--directory", "/path/to/mcp-image", "run", "mcp_image.py"] } } }

- Open Cursor and go toSettings(Navbar → Cursor Settings)
- Navigate toFeatures→MCP Servers
- Click on + Add New MCP Server
- Enter the following configuration:

{ "mcpServers": { "image": { "command": "uv", "args": ["--directory", "/path/to/mcp-image", "run", "mcp_image.py"] } } }

The server provides the following tools:

fetch_images: Fetch and process images from URLs or local file paths Parameters: image_sources: List of URLs or file paths to images Returns: List of processed images with base64 encoding and MIME types

- "Fetch these images: [list of URLs or file paths]"
- "Load and process this local image: [file_path]"

# URL-only test [ "https://upload.wikimedia.org/wikipedia/commons/thumb/7/70/Chocolate_%28blue_background%29.jpg/400px-Chocolate_%28blue_background%29.jpg", "https://imgs.search.brave.com/Sz7BdlhBoOmU4wZjnUkvgestdwmzOzrfc3GsiMr27Ik/rs:fit:860:0:0:0/g:ce/aHR0cHM6Ly9pbWdj/ZG4uc3RhYmxlZGlm/ZnVzaW9ud2ViLmNv/bS8yMDI0LzEwLzE4/LzJmOTY3NTViLTM0/YmQtNDczNi1iNDRh/LWJlMTVmNGM5MDBm/My5qcGc", "https://shigacare.fukushi.shiga.jp/mumeixxx/img/main.png" ] # Mixed URL and local file test [ "https://upload.wikimedia.org/wikipedia/commons/thumb/7/70/Chocolate_%28blue_background%29.jpg/400px-Chocolate_%28blue_background%29.jpg", "C:\\Users\\username\\Pictures\\image1.jpg", "https://imgs.search.brave.com/Sz7BdlhBoOmU4wZjnUkvgestdwmzOzrfc3GsiMr27Ik/rs:fit:860:0:0:0/g:ce/aHR0cHM6Ly9pbWdj/ZG4uc3RhYmxlZGlm/ZnVzaW9ud2ViLmNv/bS8yMDI0LzEwLzE4/LzJmOTY3NTViLTM0/YmQtNDczNi1iNDRh/LWJlMTVmNGM5MDBm/My5qcGc", "C:\\Users\\username\\Pictures\\image2.jpg" ]

- Check that all dependencies are installed correctly
- Verify that the server is running and listening for connections
- For local image loading issues, ensure the file paths are correct and accessible
- For "Unsupported image type" errors, verify the content type handling
- Look for any error messages in the server output

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see theLICENSEfile for details.

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Details

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Setting up with Highlight

From the repository

fetch_images

Claude Desktop / Cursor

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`fetch_images`