View a markdown version of this page

AgentCore 게이트웨이에서 사용 가능한 도구 나열 - Amazon Bedrock AgentCore

AgentCore 게이트웨이에서 사용 가능한 도구 나열

AgentCore 게이트웨이가 제공하는 사용 가능한 모든 도구를 나열하려면 게이트웨이의 MCP 엔드포인트에 POST 요청을 하고 요청 본문에서를 메tools/list서드로 지정합니다.

POST /mcp HTTP/1.1 Host: ${GatewayEndpoint} Content-Type: application/json Authorization: ${Authorization header} ${RequestBody}

다음 값을 교체합니다.

참고

에 대해 선택적으로 지원되는 파라미터 목록은 모델 컨텍스트 프로토콜 설명서도구에서 요청 본문의 params 객체를 tools/list 참조하세요. 검색 창 옆의 페이지 상단에서 문서를 보려는 MCP 버전을 선택할 수 있습니다. 버전이 Amazon Bedrock AgentCore에서 지원하는 버전인지 확인합니다.

응답은 이름, 설명 및 파라미터 스키마와 함께 사용 가능한 도구 목록을 반환합니다.

도구 나열을 위한 코드 샘플

게이트웨이에서 사용 가능한 도구를 나열하는 예를 보려면 다음 방법 중 하나를 선택합니다.

Python requests package
  1. import requests import json def list_tools(gateway_url, access_token): headers = { "Content-Type": "application/json", "Authorization": f"Bearer {access_token}" } payload = { "jsonrpc": "2.0", "id": "list-tools-request", "method": "tools/list" } 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 tools = list_tools(gateway_url, access_token) print(json.dumps(tools, indent=2))
MCP Client
  1. import asyncio from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client async def execute_mcp( url, token, 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. List available tools print("Listing tools...") cursor = True tools = [] while cursor: next_cursor = cursor if type(cursor) == bool: next_cursor = None list_tools_response = await session.list_tools(next_cursor) tools.extend(list_tools_response.tools) cursor = list_tools_response.nextCursor tool_names = [] if tools: for tool in tools: tool_names.append(tool.name) tool_names_string = "\n".join(tool_names) print( f"List MCP tools. # of tools - {len(tools)}" f"List of tools - \n{tool_names_string}\n" ) async def main(): url = "https://${GatewayEndpoint}/mcp" token = "your_bearer_token_here" # Optional additional headers additional_headers = { "Content-Type": "application/json", } await execute_mcp( url=url, token=token, headers=additional_headers ) # Run the async function if __name__ == "__main__": asyncio.run(main())
Strands MCP Client
  1. from strands.tools.mcp.mcp_client import MCPClient from mcp.client.streamable_http import streamablehttp_client import os def create_streamable_http_transport(mcp_url: str, access_token: str): return streamablehttp_client(mcp_url, headers={"Authorization": f"Bearer {access_token}"}) def get_full_tools_list(client): """ List tools w/ support for pagination """ more_tools = True tools = [] pagination_token = None while more_tools: tmp_tools = client.list_tools_sync(pagination_token=pagination_token) tools.extend(tmp_tools) if tmp_tools.pagination_token is None: more_tools = False else: more_tools = True pagination_token = tmp_tools.pagination_token return tools def run_agent(mcp_url: str, access_token: str): mcp_client = MCPClient(lambda: create_streamable_http_transport(mcp_url, access_token)) with mcp_client: tools = get_full_tools_list(mcp_client) print(f"Found the following tools: {[tool.tool_name for tool in tools]}") run_agent(<MCP URL>, <Access token>)
LangGraph MCP Client
  1. import asyncio from langchain_mcp_adapters.client import MultiServerMCPClient def list_tools( url, headers ): mcp_client = MultiServerMCPClient( { "agent": { "transport": "streamable_http", "url": url, "headers": headers, } } ) tools = asyncio.run(mcp_client.get_tools()) tool_details = [] tool_names = [] for tool in tools: tool_names.append(f"{tool.name}") tool_detail = f"{tool.name} - {tool.description} \n" tool_properties = tool.args_schema.get('properties', {}) properties = [] for property_name, tool_property in tool_properties.items(): properties.append(f"{property_name} - {tool_property.get('description', None)} \n") tool_details.append(f"{tool_detail}{"\n".join(properties)}") tool_details_string = "\n".join(tool_details) tool_names_string = "\n".join(tool_names) print( f"Langchain: List MCP tools. # of tools - {len(tools)}\n", f"Langchain: List of tool names - \n{tool_names_string}\n" f"Langchain: Details of tools - \n{tool_details_string}\n" )
참고

게이트웨이에서 검색이 활성화된 경우 검색 도구가 응답에 먼저 나열x_amz_bedrock_agentcore_search됩니다.