mcp测试案例

2025-09-01 12:00:47 +08:00
parent d054891ef9
commit 0740dd1aa8
3 changed files with 223 additions and 0 deletions
--- a/examples/mcp_adapters/inject_to_langgraph.py
+++ b/examples/mcp_adapters/inject_to_langgraph.py
@@ -0,0 +1,91 @@
+"""
+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())