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列出 AgentCore 閘道中的可用工具 - Amazon Bedrock AgentCore

列出 AgentCore 閘道中的可用工具

若要列出 AgentCore 閘道提供的所有可用工具,請對閘道的 MCP 端點提出 POST 請求,並在請求內文中指定 tools/list作為方法:

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

取代以下的值:

  • ${GatewayEndpoint} – 閘道的 URL,如 CreateGateway API 的回應所提供。

  • ${Authorization header} – 當您設定傳入授權時,來自身分提供者的授權憑證。

  • ${RequestBody} – 請求內文的 JSON 承載,如模型內容通訊協定 (MCP) 中的列出工具所指定。包含 tools/list做為 method

注意

如需 tools/list 選用支援的參數清單,請參閱模型內容通訊協定文件工具 請求內文中的 params 物件。在搜尋列旁的頁面頂端,您可以選取要檢視其文件的 MCP 版本。請確定 Amazon Bedrock AgentCore 支援該版本。

回應會傳回可用工具的清單,其中包含其名稱、描述和參數結構描述。

列出工具的程式碼範例

若要查看閘道中列出可用工具的範例,請選取下列其中一種方法:

範例
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" )
注意

如果在閘道上啟用搜尋,則搜尋工具x_amz_bedrock_agentcore_search會先列在回應中。