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MCP OpenVision

@Nazruden

代码仓库

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

Nazruden

关于 MCP OpenVision

MCP Server using OpenRouter models to get descriptions for images

#mcp-openvision#image-analysis#ai-assistant

MCP OpenVision 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-openvision": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@Nazruden/mcp-openvision",
        "--client",
        "claude"
      ]
    }
  }
}

MCP OpenVision 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is MCP OpenVision?

MCP OpenVision is a Model Context Protocol (MCP) server that provides image analysis capabilities powered by OpenRouter vision models. It enables AI assistants to analyze images through a simple interface within the MCP ecosystem.

How to use MCP OpenVision?

Install via pip install mcp-openvision or uv pip install mcp-openvision, or use Smithery for automatic integration with Claude Desktop. Configure by setting the OPENROUTER_API_KEY environment variable (required) and optionally OPENROUTER_DEFAULT_MODEL. Add the server to your MCP configuration file (e.g., mcp.json for Claude Desktop or Cursor) with the command uvx mcp-openvision and your API key. Test with npx @modelcontextprotocol/inspector uvx mcp-openvision.

Key features of MCP OpenVision

  • Single image_analysis tool accepting images as base64, URL, or file path.
  • Supports customizable system prompt, model, temperature, and max_tokens.
  • Works with any OpenRouter model that supports vision capabilities.
  • Default model is qwen/qwen2.5-vl-32b-instruct:free.
  • Can use relative file paths with an optional project_root parameter.
  • Integrates seamlessly with Claude Desktop and Cursor via MCP configuration.

Use cases of MCP OpenVision

  • Analyze store shelf images to identify retail products and estimate price ranges.
  • Examine medical scans for abnormalities and possible diagnoses.
  • Extract numerical data from charts and identify trends over time.
  • Transcribe text from menus, signs, or documents preserving structure.
  • Provide detailed art historical analysis of paintings focusing on composition and technique.

常见问题

1

What is required to use MCP OpenVision?

You need an OpenRouter API key and a Python environment (pip or uv). The server itself runs as an MCP tool and requires no additional services beyond OpenRouter.

2

What vision models are supported?

Any OpenRouter model that supports vision capabilities. The default is `qwen/qwen2.5-vl-32b-instruct:free`, but you can change it via the `OPENROUTER_DEFAULT_MODEL` environment variable or by passing the `model` parameter directly.

3

How can I provide images to the tool?

Images can be given as base64‑encoded strings, HTTP/HTTPS URLs, or local file paths (absolute or relative). When using relative paths, you can specify a `project_root` to resolve them against a specific directory.

4

How do I integrate MCP OpenVision with Claude Desktop?

Edit your MCP configuration file (e.g., `~/.cursor/mcp.json` or `~/Library/Application Support/Claude/claude_desktop_config.json`) and add the `openvision` server entry with command `uvx mcp-openvision` and your `OPENROUTER_API_KEY` environment variable.

5

Is there a way to test the server without a full MCP client?

Yes. Use the MCP Inspector tool by running `npx @modelcontextprotocol/inspector uvx mcp-openvision`. This will launch an interactive test interface.

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