Rufen Sie ein Tool in einem AgentCore Gateway auf

Die folgende Curl-Anfrage zeigt eine Beispielanforderung zum Aufrufen eines Tools, das searchProducts über ein Gateway mit der ID mygateway-abcdefghij aufgerufen wurde.

curl -X POST \
  https://mygateway-abcdefghij.gateway.bedrock-agentcore.us-west-2.amazonaws.com/mcp \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "id": "invoke-tool-request",
    "method": "tools/call",
    "params": {
      "name": "searchProducts",
      "arguments": {
        "query": "wireless headphones",
        "category": "Electronics",
        "maxResults": 2,
        "priceRange": {
          "min": 50.00,
          "max": 200.00
        }
      }
    }
}'

import requests
import json

def call_tool(gateway_url, access_token, tool_name, arguments):
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {access_token}"
    }

    payload = {
        "jsonrpc": "2.0",
        "id": "call-tool-request",
        "method": "tools/call",
        "params": {
            "name": tool_name,
            "arguments": arguments
        }
    }

    response = requests.post(gateway_url, headers=headers, json=payload)
    return response.json()

# Example usage
gateway_url = "https://${GatewayEndpoint}/mcp" # Replace with your actual gateway endpoint
access_token = "${AccessToken}" # Replace with your actual access token
result = call_tool(
    gateway_url,
    access_token,
    "openapi-target-1___get_orders_byId",  # Replace with <{TargetId}__{ToolName}>
    {"orderId": "ORD-12345-67890", "customerId": "CUST-98765"}
)
print(json.dumps(result, indent=2))

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
import asyncio

async def execute_mcp(
    url,
    token,
    tool_params,
    headers=None
):
    default_headers = {
        "Authorization": f"Bearer {token}"
    }
    headers = {**default_headers, **(headers or {})}

    async with streamablehttp_client(
       url=url,
       headers=headers,
    ) as (
        read_stream,
        write_stream,
        callA,
    ):
        async with ClientSession(read_stream, write_stream) as session:
            # 1. Perform initialization handshake
            print("Initializing MCP...")
            _init_response = await session.initialize()
            print(f"MCP Server Initialize successful! - {_init_response}")

            # 2. Call specific tool
            print(f"Calling tool: {tool_params['name']}")
            tool_response = await session.call_tool(
                name=tool_params['name'],
                arguments=tool_params['arguments']
            )
            print(f"Tool response: {tool_response}")
            return tool_response

async def main():
    url = "https://${GatewayEndpoint}/mcp"
    token = "your_bearer_token_here"
    tool_params = {
        "name": "LambdaTarget___get_order_tool",
        "arguments": {
            "orderId": "order123"
        }
    }
    await execute_mcp(
        url=url,
        token=token,
        tool_params=tool_params
    )


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

HINWEIS: Dies dient zum Aufrufen des Agenten

from strands.tools.mcp.mcp_client import MCPClient
from mcp.client.streamable_http import streamablehttp_client

def create_streamable_http_transport(mcp_url: str, access_token: str):
    return streamablehttp_client(mcp_url, headers={"Authorization": f"Bearer {access_token}"})

def run_agent(mcp_url: str, access_token: str):
    mcp_client = MCPClient(lambda: create_streamable_http_transport(mcp_url, access_token))

    with mcp_client:
        result = mcp_client.call_tool_sync(
            tool_use_id="tool-123",  # A unique ID for the tool call
            name="openapi-target-1___get_orders",  # The name of the tool to invoke
            arguments={}  # A dictionary of arguments for the tool
        )
        print(result)

url = {gatewayUrl}
token = {AccessToken}
run_agent(url, token)

HINWEIS: Dies dient zum Aufrufen des Agenten

import asyncio

from langgraph.prebuilt import create_react_agent

def execute_agent(
    user_prompt,
    model_id,
    region,
    tools
):
    model = ChatBedrock(model_id=model_id, region_name=region)

    agent = create_react_agent(model, tools)
    _response = asyncio.run(agent.ainvoke({
        "messages": user_prompt
    }))

    _response = _response.get('messages', {})[1].content
    print(
        f"Invoke Langchain Agents Response"
        f"Response - \n{_response}\n"
    )
    return _response