init

2025-10-17 21:40:45 +08:00
commit 7d0451131f
155 changed files with 14873 additions and 0 deletions
--- a/functionality/mcp/main.py
+++ b/functionality/mcp/main.py
@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+"""
+Demo showcasing ReAct agent with MCP tools using different transports.
+
+This example demonstrates:
+- Registering MCP tools with different transports (sse and streamable_http)
+- Using a ReAct agent with registered MCP tools
+- Getting structured output from the agent
+
+Before running this demo, please execute:
+    python mcp_servers.py
+"""
+
+import asyncio
+import json
+import os
+
+from agentscope.agent import ReActAgent
+from agentscope.formatter import DashScopeChatFormatter
+from agentscope.mcp import HttpStatefulClient, HttpStatelessClient
+from agentscope.message import Msg
+from agentscope.model import DashScopeChatModel
+from agentscope.tool import Toolkit
+from pydantic import BaseModel, Field
+
+
+class NumberResult(BaseModel):
+    """A simple number result model for structured output."""
+
+    result: int = Field(description="The result of the calculation")
+
+
+async def main() -> None:
+    """The main entry of the MCP example."""
+
+    toolkit = Toolkit()
+
+    # Create a stateful MCP client to connect to the SSE MCP server
+    # note you can also use the stateless client
+    add_mcp_client = HttpStatefulClient(
+        name="add_mcp",
+        transport="sse",
+        url="http://127.0.0.1:8001/sse",
+    )
+
+    # Create a stateless MCP client to connect to the StreamableHTTP MCP server
+    # note you can also use the stateful client
+    multiply_mcp_client = HttpStatelessClient(
+        name="multiply_mcp",
+        transport="streamable_http",
+        url="http://127.0.0.1:8002/mcp",
+    )
+
+    # The stateful client must be connected before using
+    await add_mcp_client.connect()
+
+    # Register the MCP clients to the toolkit
+    await toolkit.register_mcp_client(add_mcp_client)
+    await toolkit.register_mcp_client(multiply_mcp_client)
+
+    # Initialize the agent
+    agent = ReActAgent(
+        name="Jarvis",
+        sys_prompt="You're a helpful assistant named Jarvis.",
+        model=DashScopeChatModel(
+            model_name="qwen-max",
+            api_key=os.environ["DASHSCOPE_API_KEY"],
+        ),
+        formatter=DashScopeChatFormatter(),
+        toolkit=toolkit,
+    )
+
+    # Run the agent with a calculation task
+    res = await agent(
+        Msg(
+            "user",
+            "Calculate 2345 multiplied by 3456, then add 4567 to the result,"
+            " what is the final outcome?",
+            "user",
+        ),
+        structured_model=NumberResult,
+    )
+
+    print(
+        "Structured Output:\n"
+        "```\n"
+        f"{json.dumps(res.metadata, indent=4, ensure_ascii=False)}\n"
+        "```",
+    )
+
+    # AgentScope also allows developers to obtain the MCP tool as a local
+    #  callable object, and use it directly.
+    add_tool_function = await add_mcp_client.get_callable_function(
+        "add",
+        # If wrap the MCP tool result into the ToolResponse object in
+        #  AgentScope
+        wrap_tool_result=True,
+    )
+
+    # Call it manually
+    manual_res = await add_tool_function(a=5, b=10)
+    print("When manually calling the MCP tool function:")
+    print(manual_res)
+
+    # The stateful client should be disconnected manually!
+    await add_mcp_client.close()
+
+
+asyncio.run(main())