glm-vision-mcp

# 1. Copy and edit .env
cp .env.example .env
# Set Z_AI_API_KEY=your_coding_plan_key

# 2. Add to your MCP client
claude mcp add glm-vision-mcp \
  --env Z_AI_API_KEY=YOUR_KEY \
  -- npx -y glm-vision-mcp

claude mcp add glm-vision-mcp \
  --env Z_AI_API_KEY=YOUR_KEY \
  -- npx -y glm-vision-mcp

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "glm-vision-mcp": {
      "type": "local",
      "command": ["npx", "-y", "glm-vision-mcp"],
      "environment": { "Z_AI_API_KEY": "YOUR_KEY", "Z_AI_MODE": "ZHIPU" }
    }
  }
}

{
  "mcpServers": {
    "glm-vision-mcp": {
      "command": "npx",
      "args": ["-y", "glm-vision-mcp"],
      "env": { "Z_AI_API_KEY": "YOUR_KEY", "Z_AI_MODE": "ZHIPU" }
    }
  }
}

flowchart LR
    Client["MCP Client\n(Claude Code / OpenCode / Cline)"]
    Server["glm-vision-mcp\n(stdio transport)"]
    Cache["Result Cache\n(LRU + disk)"]
    Vision["VisionService\n(unified handler)"]
    Chat["ChatService\n(retry + backoff)"]
    API["GLM Vision API\n(ZHIPU / ZAI)"]

    Client --> Server
    Server --> Cache
    Cache -->|"miss"| Vision
    Cache -->|"hit"| Server
    Vision --> Chat
    Chat --> API
    API --> Vision

src/
├── core/              # Environment, chat, vision, file, cache services
│   ├── environment.ts   # Dotenv + dual-platform + key fallback
│   ├── chat-service.ts  # GLM API calls with smart retry
│   ├── vision-service.ts  # Unified analysis orchestration
│   ├── file-service.ts  # Validation, base64 encoding, fingerprinting
│   └── cache.ts         # LRU + disk cache
├── tools/
│   ├── definitions.ts   # 8-tool data-driven definitions
│   └── registry.ts      # Tool registration on MCP server
├── prompts/             # 8 specialized system prompts
├── types/               # Error type hierarchy
└── utils/               # Logger, sanitization, validation
tests/
scripts/
  └── smoke.ts           # End-to-end verification with real key