"""
Full example: Start a FastMCP-style HTTP MCP server, then use
langchain-mcp-adapters to inject MCP tools into a LangGraph + Qwen agent.

Steps:
1) Start the local HTTP MCP server (fallback minimal):
   - uv pip install fastapi uvicorn
   - uvicorn examples.mcp_adapters.fastmcp_server:http_app --host 127.0.0.1 --port 8010

   The MCP endpoint is available at: http://127.0.0.1:8010/mcp/

2) Install mcp-adapters:
   - uv pip install -e '.[mcp-adapters]'

3) Configure adapter entry + config (choose one):
   A) Explicit entry (recommended):
      export MCP_ADAPTER_ENTRY='langchain_mcp_adapters:create_tools'
      export MCP_CONFIG_JSON='{"servers":{"local":{"url":"http://127.0.0.1:8010/mcp/","transport":"streamable_http"}}}'

   B) If your adapter provides a different function:
      export MCP_ADAPTER_ENTRY='your_module:your_entry'
      export MCP_CONFIG_JSON='{}'

4) Qwen env (or .env auto-loaded):
   - QWEN_API_KEY, QWEN_BASE_URL, QWEN_MODEL, ...

5) Run this example:
   - python examples/mcp_adapters/inject_to_langgraph.py
"""

import json
import os
import importlib
from typing import Any, Dict, List

import asyncio
from langchain_core.messages import HumanMessage
from langgraph_qwen.chat_model import ChatQwenOpenAICompat
from langgraph.prebuilt import create_react_agent

def _env(name: str, default: str = "") -> str:
    v = os.getenv(name)
    return v if v else default

async def _load_tools_via_client() -> List[Any]:
    try:
        from langchain_mcp_adapters.client import MultiServerMCPClient  # type: ignore
    except Exception as e:
        raise RuntimeError("Please install langchain-mcp-adapters: uv pip install -e '.[mcp-adapters]'") from e

    weather_url = _env("WEATHER_MCP_URL", "http://localhost:8000/mcp")
    weather_transport = _env("WEATHER_TRANSPORT", "streamable_http")

    client = MultiServerMCPClient(
        {
            "weather": {
                "url": weather_url,
                "transport": weather_transport,
            }
        }
    )
    tools = await client.get_tools()
    # Best-effort cleanup if client exposes a close method
    try:
        if hasattr(client, "close") and callable(getattr(client, "close")):
            await client.close()  # type: ignore
        elif hasattr(client, "close_all_sessions") and callable(getattr(client, "close_all_sessions")):
            await client.close_all_sessions()  # type: ignore
    except Exception:
        pass
    return tools

async def main():
    tools = await _load_tools_via_client()
    print("Discovered tools:")
    for t in tools:
        print(" -", getattr(t, "name", "<noname>"))

    model = ChatQwenOpenAICompat(temperature=0).bind(tool_choice="auto")
    # 或直接：model = ChatQwenOpenAICompat(temperature=0).bind_tools(tools).bind(tool_choice="auto")
    agent = create_react_agent(model, tools)

    prompt = (
        "请先列出可用工具名，然后选择一个合理的工具做一次演示调用，并用简洁中文总结结果。"
    )
    res = await agent.ainvoke({"messages": [HumanMessage(content=prompt)]})
    print("=== Final ===")
    print(res["messages"][-1].content)

if __name__ == "__main__":
    asyncio.run(main())