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glm-vision-mcp

封装智谱 GLM-4.6V-Flash(免费视觉模型)的 MCP 服务器,向任意 MCP 客户端暴露一个 analyze_image 工具,支持单图/多图分析、OCR、多图对比。

特性

能力 说明
图片分析 本地路径 / http(s) URL / base64 data URI 均可,自动转 data URI
多图对比 多张图片一次传入,按 prompt 对比分析
限流韧性 429 / 1302 / 1305 指数退避重试 → 多 Key 轮询 → 降级备用模型 glm-4.1v-thinking-flash
配置自查 check_config 工具检查 Key/模型/端点,不泄露 Key

环境要求

  • Python >= 3.10
  • 智谱开放平台 API Key(https://open.bigmodel.cn/usercenter/apikeys),glm-4.6v-flash 免费
  • 想进一步降低限流概率,可注册多个账号各取一个 Key,配置时用英文逗号分隔

安装

cd glm-vision-mcp
python -m venv .venv
.venv\Scripts\pip install -r requirements.txt

启动

# stdio 模式(MCP 客户端默认方式)
$env:ZHIPU_API_KEY = "你的Key"
.venv\Scripts\python server.py

# SSE 调试模式(无鉴权,仅限本机)
.venv\Scripts\python server.py --sse 8090

客户端配置

Codex(~/.codex/config.toml

[mcp_servers.glm-vision]
command = "C:\\绝对路径\\glm-vision-mcp\\.venv\\Scripts\\python.exe"
args = ["C:\\绝对路径\\glm-vision-mcp\\server.py"]

[mcp_servers.glm-vision.env]
ZHIPU_API_KEY = "你的Key"
# GLM_VISION_MODELS = "glm-4.6v-flash"
# GLM_API_BASE = "https://open.bigmodel.cn/api/paas/v4/chat/completions"

Claude Desktop(claude_desktop_config.json

{
  "mcpServers": {
    "glm-vision": {
      "command": "C:\\绝对路径\\glm-vision-mcp\\.venv\\Scripts\\python.exe",
      "args": ["C:\\绝对路径\\glm-vision-mcp\\server.py"],
      "env": { "ZHIPU_API_KEY": "你的Key" }
    }
  }
}

工具接口

analyze_image(images, prompt, temperature, max_tokens, thinking)

参数 类型 必填 说明
images string[] 本地路径 / http(s) URL / data URI
prompt string 分析要求,默认“请详细描述这张图片的内容”
temperature number 0.0~1.0,默认 0.7
max_tokens integer 最大输出 token,默认 2048
thinking boolean 深度思考模式,默认 false

环境变量

变量 必填 说明
ZHIPU_API_KEY 智谱 API Key,逗号分隔支持多 Key 轮询
GLM_VISION_MODELS 模型优先级,逗号分隔,默认 glm-4.6v-flash,glm-4.1v-thinking-flash
GLM_API_BASE 覆盖 API 端点

注意事项

  • 本地图片单张 ≤ 10MB,支持 jpg/jpeg/png/webp/gif/bmp
  • 免费模型高峰期可能限流,全部 Key + 全部模型都限流时才报错,稍等 15~30 秒错峰重试
  • 401/400 等非限流错误不降级不重试,直接返回,便于定位配置问题

验证

# 离线检查(不联网)
.venv\Scripts\python test_smoke.py

# 联网冒烟:MCP 握手 + analyze_image 真实调用
$env:ZHIPU_API_KEY = "你的Key"
.venv\Scripts\python test_smoke.py --live

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