VL 视觉理解 MCP 服务 - 基于 tokendance 的 Qwen2.5-VL 模型
Project description
VL MCP Server
基于 MCP(Model Context Protocol)协议的视觉理解服务,通过 tokendance 平台调用 Qwen2.5-VL 模型,实现图片内容分析功能。
功能特性
- 支持图片 URL 和 Base64 编码输入
- 基于 Qwen2.5-VL-7B-Instruct 模型
- 兼容 Cherry Studio、Claude Code、Cursor 等 MCP 客户端
- 可托管部署到魔搭 MCP 广场
安装使用
环境要求
- Python >= 3.10
- uv(推荐)或 pip
配置
设置环境变量:
export TOKENDANCE_API_KEY="your-api-key"
MCP 客户端配置
Cherry Studio / Claude Desktop / Cursor:
{
"mcpServers": {
"vl-image-analyzer": {
"command": "uvx",
"args": ["vl-mcp-server"],
"env": {
"TOKENDANCE_API_KEY": "your-api-key"
}
}
}
}
开发
本地开发
# 克隆仓库
git clone <repo-url>
cd vl-mcp-server
# 使用 uv 安装依赖
uv sync
# 运行服务
uv run vl-mcp-server
调试
使用 MCP Inspector:
npx @modelcontextprotocol/inspector uv run vl-mcp-server
工具说明
analyze_image
分析图片内容并返回描述。
参数:
image(string, 必填): 图片 URL 或 Base64 编码字符串image_type(string, 可选): 图片类型,"url" 或 "base64",默认 "url"prompt(string, 可选): 自定义分析提示词,默认 "请详细描述这张图片的内容"
示例:
{
"image": "https://example.com/image.jpg",
"image_type": "url",
"prompt": "描述这张图片的主要内容"
}
许可证
MIT License
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