┌─────────────────────────────────────────────┐
                │              router_core.py                 │
                │                                             │
   task_desc ──▶│  classify_task()  →  5 difficulty levels   │
   content   ──▶│  _get_candidates() → ordered provider chain│
   image ──────▶│  route_and_call()  → free-first, fallback  │
                │                                             │
                └──────────────┬──────────────────────────────┘
                               │
              ┌────────────────┼───────────────────┐
              ▼                ▼                   ▼
        auto_router.py    mcp_server.py       Python API
        (CLI + daemon)    (MCP tools)         (import router_core)

# 1. Install (only requests is required)
pip install requests

# 2. Configure
cp config.example.json config.json
#    → fill in your API keys

# 3. CLI — analyze only (zero cost, no model call)
python router_core.py analyze "分析这份气象数据"

# 4. CLI — route and call
python router_core.py call "翻译以下段落" --content "Hello world" --system "你是专业翻译"

# 5. Python API
from router_core import route_and_call, analyze_task
result = analyze_task("写一份论文摘要", content_len=300)
print(result["primary"])           # first candidate
print(result["candidates"])        # full fallback chain
text = route_and_call("总结要点", "long text...")["content"]

python mcp_server.py

{
  "mcpServers": {
    "model-router": {
      "command": "python",
      "args": ["/path/to/model-router/mcp_server.py"],
      "env": {"PYTHONIOENCODING": "utf-8"}
    }
  }
}

# One-shot
python auto_router.py "任务描述" -c "内容" --image photo.png

# Daemon: drop task files into inbox/, results appear in outbox/
python auto_router.py --watch --dir ./tasks