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使用自然语言查询在 AgentCore 网关中搜索工具 - 亚马逊基岩 AgentCore

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使用自然语言查询在 AgentCore 网关中搜索工具

如果您在创建网关时启用了语义搜索,则可以通过自然语言查询调用该x_amz_bedrock_agentcore_search工具在网关中搜索工具。当你有许多工具并且需要为你的用例找到最合适的工具时,语义搜索特别有用。要了解如何在网关创建期间启用语义搜索,请参阅创建 Amazon Bedrock AgentCore 网关。

支持 AWS 语义搜索区域

以下 AWS 区域支持语义搜索:

区域名称 Region

美国东部(弗吉尼亚州北部)

us-east-1

美国东部(俄亥俄州)

us-east-2

美国西部(俄勒冈州)

us-west-2

亚太地区(海得拉巴)

ap-south-2

亚太地区(孟买)

ap-south-1

亚太地区(首尔)

ap-northeast-2

亚太地区(新加坡)

ap-southeast-1

亚太地区(悉尼)

ap-southeast-2

亚太地区(东京)

ap-northeast-1

加拿大(中部)

ca-central-1

欧洲地区(法兰克福)

eu-central-1

欧洲地区(爱尔兰)

eu-west-1

欧洲地区(伦敦)

eu-west-2

欧洲地区(米兰)

eu-south-1

欧洲地区(巴黎)

eu-west-3

欧洲(西班牙)

eu-south-2

欧洲地区(斯德哥尔摩)

eu-north-1

南美洲(圣保罗)

sa-east-1

要使用此工具搜索 AgentCore 工具,请使用该tools/call方法向网关的 MCP 端点发出以下 POST 请求:

例
2025-11-25 and earlier
POST /mcp HTTP/1.1 Host: ${GatewayEndpoint} Accept: application/json, text/event-stream Content-Type: application/json Authorization: ${Authorization header} MCP-Protocol-Version: ${McpProtocolVersion} { "jsonrpc": "2.0", "id": "${RequestName}", "method": "tools/call", "params": { "name": "x_amz_bedrock_agentcore_search", "arguments": { "query": ${Query} } } }
2026-07-28

在版本上2026-07-28,每个请求都带有MCP-Protocol-Version标头、Mcp-Method和Mcp-Name请求元数据标头以及正_meta文中的版本字段。

POST /mcp HTTP/1.1 Host: ${GatewayEndpoint} Accept: application/json, text/event-stream Content-Type: application/json Authorization: ${Authorization header} MCP-Protocol-Version: 2026-07-28 Mcp-Method: tools/call Mcp-Name: x_amz_bedrock_agentcore_search { "jsonrpc": "2.0", "id": "${RequestName}", "method": "tools/call", "params": { "name": "x_amz_bedrock_agentcore_search", "arguments": { "query": ${Query} }, "_meta": { "io.modelcontextprotocol/protocolVersion": "2026-07-28", "io.modelcontextprotocol/clientInfo": { "name": "my-agent", "version": "1.0.0" }, "io.modelcontextprotocol/clientCapabilities": {} } } }
注意

网关仅接受其protocolConfiguration.mcp配置supportedVersions字段中列出的 MCP 协议版本。要使用版本2026-07-28,请确保您的网关supportedVersions包含该版本。您可以使用 UpdateGateway API 更改支持的版本。

替换以下值:

  • ${GatewayEndpoint}— 网关的 URL,如 CreateGateway API 的响应中所提供。

  • ${Authorization header}— 设置入站授权时身份提供商提供的授权凭证。

  • ${McpProtocolVersion}— 请求的 MCP 协议版本,例如2025-11-25。该版本必须是您的网关支持的版本。

  • ${RequestName}— 请求的名称。

  • ${Query}— 用于搜索工具的自然语言查询。

响应返回与查询相关的工具列表。

工具搜索的代码示例

要查看使用自然语言查询在网关中查找工具的示例,请选择以下方法之一:

例
Python requests package (2025-11-25 and earlier)

将标MCP-Protocol-Version头设置为您的网关支持的版本。

import requests import json def search_tools(gateway_url, access_token, query): headers = { "Content-Type": "application/json", "Authorization": f"Bearer {access_token}", "MCP-Protocol-Version": "2025-11-25" } payload = { "jsonrpc": "2.0", "id": "search-tools-request", "method": "tools/call", "params": { "name": "x_amz_bedrock_agentcore_search", "arguments": { "query": query } } } 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 results = search_tools(gateway_url, access_token, "find order information") print(json.dumps(results, indent=2))
Python requests package (2026-07-28)

在版本上2026-07-28,在正文中包括Mcp-Method和Mcp-Name请求元数据标头以及_meta版本字段。标MCP-Protocol-Version题必须匹配_meta.io.modelcontextprotocol/protocolVersion。您的网关supportedVersions必须包括2026-07-28。

import requests import json def search_tools(gateway_url, access_token, query): headers = { "Content-Type": "application/json", "Authorization": f"Bearer {access_token}", "MCP-Protocol-Version": "2026-07-28", "Mcp-Method": "tools/call", "Mcp-Name": "x_amz_bedrock_agentcore_search" } payload = { "jsonrpc": "2.0", "id": "search-tools-request", "method": "tools/call", "params": { "name": "x_amz_bedrock_agentcore_search", "arguments": { "query": query }, "_meta": { "io.modelcontextprotocol/protocolVersion": "2026-07-28", "io.modelcontextprotocol/clientInfo": {"name": "my-agent", "version": "1.0.0"}, "io.modelcontextprotocol/clientCapabilities": {} } } } 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 results = search_tools(gateway_url, access_token, "find order information") print(json.dumps(results, indent=2))
MCP Client
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": "x_amz_bedrock_agentcore_search", "arguments": { "query": "How do I find order details?" } } await execute_mcp( url=url, token=token, tool_params=tool_params ) if __name__ == "__main__": asyncio.run(main())
Strands MCP Client
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 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]}") result = mcp_client.call_tool_sync( tool_use_id="tool-123", # A unique ID for the tool call name="x_amz_bedrock_agentcore_search", # The name of the tool to invoke arguments={"query": "find order information"} # A dictionary of arguments for the tool ) print(result) url = {gatewayUrl} token = {AccessToken} run_agent(url, token)
LangGraph MCP Client
import asyncio from langchain_mcp_adapters.client import MultiServerMCPClient from langgraph.prebuilt import create_react_agent url = "" headers = {} def filter_search_tool( ): mcp_client = MultiServerMCPClient( { "agent": { "transport": "streamable_http", "url": url, "headers": headers, } } ) tools = asyncio.run(mcp_client.get_tools()) builtin_search_tool = [] for tool in tools: if tool.name == "x_amz_bedrock_agentcore_search": builtin_search_tool.append(tool) return builtin_search_tool def execute_agent( user_prompt, model_id, region, tools ): model = ChatBedrock(model_id=model_id, region_name=region) agent = create_react_agent(model, filter_search_tool()) _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