What are the prerequisites for running the server?
Python 3.10 or later and the uv package manager are required.
MCP Server - Image
@IA-Programming
Basic information
About MCP Server - Image
## MCP-Images Looking for a powerful image processing server? MCP Server-Image provides enterprise-grade image handling with just a few lines of code. Perfect for AI applications, web services, and data processing pipelines. [Get Started](#installation) | [Support Us](
MCP Server - Image config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-images": {
"command": "uv",
"args": [
"venv"
]
}
}
}MCP Server - Image tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is MCP Server - Image?
A Model Context Protocol (MCP) server that provides tools for fetching and processing images from URLs, local file paths, and numpy arrays. It includes a tool called fetch_images that returns images as base64-encoded strings along with their MIME types.
How to use MCP Server - Image?
Prerequisites: Python 3.10+ and the uv package manager. Clone the repository, create and activate a virtual environment with uv, then install dependencies from requirements.txt. Run the server directly with uv run python mcp_image.py. For integration with Windsurf or Cursor, add a JSON configuration entry pointing to the server script.
Key features of MCP Server - Image
- Fetch images from HTTP/HTTPS URLs
- Load images from local file paths
- Specialized handling for large local images
- Automatic image compression for images >1MB
- Parallel processing of multiple images
- Proper MIME type mapping for file extensions
- Comprehensive error handling and logging
Use cases of MCP Server - Image
- Fetch and process images from multiple URLs in a single request
- Load and process local images for AI assistant workflows
- Combine URL and local file images for unified processing
Frequently asked questions
How do I configure the server for Windsurf or Cursor?
Edit the `mcp_config.json` file for Windsurf or add a new MCP server in Cursor settings, providing the command `uv` with arguments pointing to the server directory and script.
What does the fetch_images tool return?
It returns a list of processed images, each as a base64-encoded string with its corresponding MIME type.
How are large images handled?
Images larger than 1MB are automatically compressed to reduce size.
What image sources are supported?
Images can be sourced from URLs (http/https), local file paths, and numpy arrays.
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