本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

# 创建使用您的 AgentCore 网关的代理
<a name="gateway-agent-integration"></a>

创建和测试网关后，您可以创建 AI 代理并将其连接到网关。连接到您的网关的代理能够调用网关中的工具，并使用 [ Amazon Bedrock 模型](https://docs.amazonaws.cn/bedrock/latest/userguide/models-supported.html)来响应查询。

要了解如何创建代理、将其连接到网关并调用它来回答查询，请选择以下方法之一：

**Example**  

1. 

   ```
   from strands import Agent
   from strands.models import BedrockModel
   from strands.tools.mcp.mcp_client import MCPClient
   from mcp.client.streamable_http import streamablehttp_client
   
   def _invoke_agent(
       bedrock_model,
       mcp_client,
       prompt
   ):
       with mcp_client:
           tools = mcp_client.list_tools_sync()
           agent = Agent(
               model=bedrock_model,
               tools=tools
           )
           return agent(prompt)
   
   def _create_streamable_http_transport(headers=None):
       url = {gatewayUrl}
       access_token = {AccessToken}
       headers = {**headers} if headers else {}
       headers["Authorization"] = f"Bearer {access_token}"
       return streamablehttp_client(
           url,
           headers=headers
       )
   
   def _get_bedrock_model(model_id):
       return BedrockModel(
           inference_profile_id=model_id,
           temperature=0.0,
           streaming=True,
      )
   
   mcp_client = MCPClient(_create_streamable_http_transport)
   
   if __name__ == "__main__":
       user_prompt = "What orders do I have?"
       _response = _invoke_agent(
           bedrock_model=_get_bedrock_model("us.anthropic.claude-sonnet-4-20250514-v1:0"),
           mcp_client=mcp_client,
           prompt=user_prompt
       )
       print(_response)
   ```

1. 

   ```
   from mcp import ClientSession
   from mcp.client.streamable_http import streamablehttp_client
   
   from langgraph.prebuilt import create_react_agent
   from langchain_mcp_adapters.tools import load_mcp_tools
   
   # Replace with actual values and the Amazon Bedrock model of your choice
   gateway_url = "${GatewayUrl}"
   access_token = "${AccessToken}"
   model = ChatBedrock(model_id="anthropic.claude-3-sonnet-20240229-v1:0", region_name="us-west-2")
   
   async with streamablehttp_client(gateway_url, headers={"Authorization": f"Bearer {access_token}"}) as (read, write, _):
       async with ClientSession(read, write) as session:
           # Initialize the connection
           await session.initialize()
   
           # Get tools
           tools = await load_mcp_tools(session)
           agent = create_react_agent(model, tools)
           math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
   ```

1. 

   ```
   #!/bin/bash
   # Script to add MCP server to Claude
   
   # Server configuration
   SERVER_NAME=${ServerName} # Write your server name
   GATEWAY_MCP_SERVER_URL=${GatewayUrl} # The gateway MCP URL
   AUTH_TOKEN=${AuthToken} # Claude authentication token
   
   echo "Adding MCP server to Claude..."
   echo "Server Name: $SERVER_NAME"
   echo "Server URL: $SERVER_URL"
   echo ""
   
   # Add the MCP server
   claude mcp add "$SERVER_NAME" "$GATEWAY_MCP_SERVER_URL" \
     --transport http \
     --header "Authorization: Bearer $AUTH_TOKEN"
   
   # Check if the command was successful
   if [ $? -eq 0 ]; then
       echo "MCP server added successfully!"
       echo ""
       echo "You can now check mcp server health with: claude mcp list"
   else
       echo "Failed to add MCP server"
       exit 1
   fi
   ```