image-recognition-mcp

@shalevshalit/image-recognition-mcp

1 Stars 31 次浏览 shalevshalit 更新于 2026-08-23

MCP server for AI-powered image recognition and description using OpenAI vision models.

MCP 服务配置

复制以下 JSON 到 OPClaw 或其他 MCP 客户端的配置文件中即可使用

{
  "mcpServers": {
    "image-recognition-mcp": {
      "args": [
        "image-recognition-mcp@1.0.0"
      ],
      "command": "npx"
    }
  }
}

可用工具（5 个）

该服务在 MCP 协议中暴露的工具，AI 可按需调用

tavily_search 14 个参数需填 1 项 Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.

Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.

必填参数：query

tavily_extract 6 个参数需填 1 项 Extract content from URLs. Returns raw page content in markdown or text format.

Extract content from URLs. Returns raw page content in markdown or text format.

必填参数：urls

tavily_crawl 11 个参数需填 1 项 Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.

Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.

必填参数：url

tavily_map 8 个参数需填 1 项 Map a website's structure. Returns a list of URLs found starting from the base URL.

Map a website's structure. Returns a list of URLs found starting from the base URL.

必填参数：url

tavily_research 2 个参数需填 1 项 Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.

Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.

必填参数：input

服务介绍

Image Recognition MCP Server

A Model Context Protocol (MCP) server that provides AI-powered image recognition and description capabilities using OpenAI's vision models.

# Overview

This MCP server enables AI assistants to analyze and describe images through a simple URL-based interface. It leverages OpenAI's powerful vision models to provide detailed descriptions of images, making it easy to integrate image analysis capabilities into your AI workflows.

# Features

Image Analysis: Analyze images from URLs and get detailed descriptions
OpenAI Integration: Uses OpenAI's latest vision models for accurate image recognition
MCP Protocol: Fully compatible with the Model Context Protocol standard
TypeScript: Built with TypeScript for type safety and better development experience
Simple API: Easy-to-use interface for image description requests

# Installation

# # Prerequisites

Node.js 18+
npm or yarn
OpenAI API key

# # MCP Client Configuration

To use this server with an MCP client, add the following configuration:

{
  "mcpServers": {
    "image-recognition": {
      "command": "npx",
      "args": ["-y", "@mcp-s/image-recognition-mcp"],
      "env": {
        "OPENAI_API_KEY": "your-actual-openai-api-key-here"
      }
    }
  }
}

⚠️ IMPORTANT: The env section with your OpenAI API key is required - this is the only way the MCP server can function.

# Usage

# # Available Tools

# # # `describe-image`

Analyzes an image from a URL and provides a detailed description.

Parameters:

imageUrl (string): The URL of the image to analyze

Example:

{
  "tool": "describe-image",
  "arguments": {
    "imageUrl": "https://example.com/image.jpg"
  }
}

Response:

{
  "content": [
    {
      "type": "text",
      "text": "The image shows a beautiful sunset over a mountain landscape with vibrant orange and pink colors in the sky..."
    }
  ]
}

# # Integration with AI Assistants

This MCP server can be integrated with various AI assistants that support the MCP protocol, such as:

Claude Desktop
Other MCP-compatible AI systems

# Development

# # Project Structure

image-recognition-mcp/
├── src/
│   └── index.ts          #  Main server implementation
├── dist/                 #  Compiled JavaScript output
├── package.json          #  Project dependencies and scripts
├── tsconfig.json         #  TypeScript configuration
└── README.md            #  This file

# # Error Handling

The server includes robust error handling for:

Invalid image URLs
Network connectivity issues
OpenAI API errors
Invalid input parameters

# Troubleshooting

# # Common Issues

Server fails to start or doesn't work:

✅ Check if OpenAI API key is set: This is the # 1 cause of issues
```
echo $OPENAI_API_KEY  #  Should show your API key
```
✅ Verify API key is valid: Test with OpenAI's API directly
✅ Check API key has sufficient credits: Ensure your OpenAI account has available credits

"Authentication failed" errors:

The OpenAI API key is missing or invalid
Set the environment variable: export OPENAI_API_KEY="your-key"

# Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

# License

This project is licensed under the ISC License. See the LICENSE file for details.

# Support

For support, please open an issue in the GitHub repository or contact the maintainer.

查看源码项目主页