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Image Description MCP Server

Model Context Protocol (MCP) server that enables AI assistants to analyze images using xAI's Grok vision API. Supports URL and local file processing with OCR capabilities.

Unclaimed last commit 11 months ago ai
43Fair

Scored 3 months ago · breakdown

About Image Description MCP Server

Image Description MCP Server is an MCP server published by 7etsuo in the AI category: model Context Protocol (MCP) server that enables AI assistants to analyze images using xAI's Grok vision API. Supports URL and local file processing with OCR capabilities. It has been installed 0 times through Conduid.

The repository has 7 stars and 2 forks, with the last commit 11 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

Install

Install
npx image-description-mcp-server

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

Image Description MCP Server

A Model Context Protocol (MCP) server that provides AI-powered image analysis using xAI's Grok API.

Purpose

This MCP server provides a secure interface for AI assistants to analyze images using Grok's advanced vision capabilities. It supports both web-hosted images and local files, offering detailed descriptions, technical metadata extraction, and optical character recognition (OCR).

Features

Current Implementation

  • describe_image_url - Analyzes images from web URLs and provides AI-generated descriptions
  • describe_image_file - Analyzes local image files and provides AI-generated descriptions
  • extract_text_from_image - Performs OCR to extract readable text from images

Prerequisites

  • Docker Desktop with MCP Toolkit enabled
  • Docker MCP CLI plugin (docker mcp command)
  • Grok API key from https://console.x.ai/

Installation

See the step-by-step instructions provided with the files.

Usage Examples

In Grok4 Code Fast, you can ask:

Local Testing

# Set environment variables for testing
export GROK_API_KEY="your-grok-api-key"

# Run directly
python image-description-mcp_server.py

# Test MCP protocol
echo '{"jsonrpc":"2.0","method":"tools/list","id":1}' | python image-description-mcp_server.py

Adding New Tools

  1. Add the function to image-description-mcp_server.py
  2. Decorate with @mcp.tool()
  3. Update the catalog entry with the new tool name
  4. Rebuild the Docker image

Troubleshooting

Authentication Errors

  • Verify secrets with docker mcp secret list
  • Ensure GROK_API_KEY is set correctly
  • Check API key validity at https://console.x.ai/

Image Processing Errors

  • Ensure image URLs are accessible and valid
  • Check local file paths exist and are readable
  • Verify image formats are supported (JPEG, PNG, WebP, etc.)

Security Considerations

  • All secrets stored in Docker Desktop secrets
  • Never hardcode API keys
  • Running as non-root user in Docker
  • Images processed temporarily in memory
  • No permanent storage of image data
  • Sensitive data never logged

API Documentation

This service integrates with xAI's Grok API:

Data Sources

External Image URLs

  • Source: Web-hosted images accessible via HTTP/HTTPS
  • Access Method: HTTP GET requests using httpx
  • Purpose: Download images for analysis from any public URL
  • Limitations: Only accessible URLs; no authentication-protected images

Local Image Files

  • Source: Filesystem access to local image files
  • Access Method: Python file I/O
  • Purpose: Analyze images stored locally on the user's system
  • Supported Paths: Absolute and relative file paths
  • Supported Formats: JPEG, PNG, WebP, TIFF, GIF, BMP

Grok API

  • Source: xAI's Grok model with vision capabilities
  • Access Method: REST API calls via httpx
  • Purpose: AI-powered image analysis and description generation
  • Data Flow: Images converted to base64, sent to Grok, receive structured analysis

Image Processing

  • Source: PIL (Pillow) and OpenCV libraries
  • Access Method: Local processing
  • Purpose: Extract technical metadata and perform OCR
  • No External Calls: Pure local processing

License

MIT License

README mirrored from the source repository 3 months ago. The original is authoritative.

Questions

About Image Description MCP Server

How do I install Image Description MCP Server?

Run npx image-description-mcp-server, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Image Description MCP Server safe to use with an AI agent?

Its trust score is 43 out of 100 (fair). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Image Description MCP Server still maintained?

The last commit was 11 months ago, with 0 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.