🚀 **加速AI代理开发流程**：Amazon Bedrock AgentCore MCP Server通过提供内置的运行时、网关集成、身份管理和代理内存支持，旨在显著缩短与Bedrock AgentCore兼容组件的开发时间。它允许开发者进行快速原型设计、构建生产级AI解决方案，并支持企业级代理基础设施的扩展。

💡 **简化开发体验**：结合Agentic IDEs（如Kiro、Claude Code、GitHub Copilot和Cursor）以及MCP服务器，开发者可以通过自然语言指令完成过去耗时费力的工作，例如学习Bedrock AgentCore服务、集成Runtime和Tools Gateway、管理安全配置以及部署到生产环境，将这些过程从数小时或数天缩短到几分钟。

⚙️ **自动化开发环境配置**：AgentCore MCP服务器能够自动化处理开发环境的完整设置过程，包括安装必要的依赖项（如bedrock-agentcore SDK）、配置AWS凭证和区域、定义执行角色及其权限、设置ECR存储库，以及创建必要的配置文件（如.bedrock_agentcore.yaml），从而消除了开发者在环境搭建上的负担。

🔗 **无缝工具集成与调用**：该服务器简化了与Bedrock AgentCore Gateway的工具集成，实现了云环境中代理与工具之间的无缝通信。开发者可以使用自然语言命令，通过编码助手调用已配置的代理，并验证完整的执行流程，包括与AgentCore Gateway工具的交互。

🧱 **分层架构增强上下文**：文章提出了一种分层架构，从Agentic IDEs到AWS服务文档、框架文档、SDK文档，再到特定任务的引导文件，每一层都为编码助手提供更具体、更深度的上下文信息，使其能够处理从基础AWS操作到复杂代理转换和部署的各种任务。

Today, we’re excited to announce the Amazon Bedrock AgentCore Model Context Protocol (MCP) Server. With built-in support for runtime, gateway integration, identity management, and agent memory, the AgentCore MCP Server is purpose-built to speed up creation of components compatible with Bedrock AgentCore. You can use the AgentCore MCP server for rapid prototyping, production AI solutions, or to scale your agent infrastructure for your enterprise.

Agentic IDEs like Kiro, Claude Code, GitHub Copilot, and Cursor, along with sophisticated MCP servers are transforming how developers build AI agents. What typically takes significant time and effort, for example learning about Bedrock AgentCore services, integrating Runtime and Tools Gateway, managing security configurations, and deploying to production can now be completed in minutes through conversational commands with your coding assistant.

In this post we introduce the new AgentCore MCP server and walk through the installation steps so you can get started.

AgentCore MCP server capabilities

The AgentCore MCP server brings a new agentic development experience to AWS, providing specialized tools that automate the complete agent lifecycle, eliminate the steep learning curve, and reduce development friction that can slow innovation cycles. To address specific agent development challenges the AgentCore MCP server:

Transforms agents for AgentCore Runtime integration by providing guidance to your coding assistant on the minimum functionality changes needed—adding Runtime library imports, updating dependencies, initializing apps with BedrockAgentCoreApp(), converting entrypoints to decorators, and changing direct agent calls to payload handling—while preserving your existing agent logic and Strands Agents features. Automates development environment provisioning by handling the complete setup process through your coding assistant: installing required dependencies (bedrock-agentcore SDK, bedrock-agentcore-starter-toolkit CLI helpers, strands-agents SDK), configuring AWS credentials and AWS Regions, defining execution roles with Bedrock AgentCore permissions, setting up ECR repositories, and creating .bedrock_agentcore.yaml configuration files. Simplifies tool integration with Bedrock AgentCore Gateway for seamless agent-to-tool communication in the cloud environment. Enables simple agent invocation and testing by providing natural language commands through your coding assistant to invoke provisioned agents on AgentCore Runtime and verify the complete workflow, including calls to AgentCore Gateway tools when applicable.

Layered approach

When using the AgentCore MCP server with your favorite client, we encourage you to consider a layered architecture designed to provide comprehensive AI agent development support:

Layer 1: Agentic IDE or client – Use Kiro, Claude Code, Cursor, VS Code extensions, or another natural language interface for developers. For very simple tasks, agentic IDEs are equipped with the right tools to look up documentation and perform tasks specific to Bedrock AgentCore. However, with this layer alone, developers may observe sub-optimal performance across AgentCore developer paths. Layer 2: AWS service documentation – Install the AWS Documentation MCP Server for comprehensive AWS service documentation, including context about Bedrock AgentCore. Layer 3: Framework documentation – Install the Strands, LangGraph, or other framework docs MCP servers or use the llms.txt for framework-specific context. Layer 4: SDK documentation – Install the MCP or use the llms.txt for the Agent Framework SDK and Bedrock AgentCore SDK for a combined documentation layer that covers the Strands Agents SDK documentation and Bedrock AgentCore API references. Layer 5: Steering files – Task-specific guidance for more complex and repeated workflows. Each IDE has a different approach to using steering files (for example, see Steering in the Kiro documentation).

Each layer builds upon the previous one, providing increasingly specific context so your coding assistant can handle everything from basic AWS operations to complex agent transformations and deployments.

Installation

To get started with the Amazon Bedrock AgentCore MCP server you can use the one-click install on the Github repository.

Each IDE integrates with an MCP differently using the mcp.json file. Review the MCP documentation for your IDE, such as Kiro, Cursor, Q CLI, and Claude Code to determine the location of the mcp.json.

Use the following in your mcp.json:

{  "mcpServers": {    "awslabs.amazon-bedrock-agentcore-mcp-server": {      "command": "uvx",      "args": ["awslabs.amazon-bedrock-agentcore-mcp-server@latest"],      "env": {        "FASTMCP_LOG_LEVEL": "ERROR"      },      "disabled": false,      "autoApprove": []    }  }}

For example, here is what the IDE looks like on Kiro, with the AgentCore MCP server and the two tools, search_agentcore_docs and fetch_agentcore_doc, connected:

Using the AgentCore MCP server for agent development

While we show demos for various use cases below using the Kiro IDE, the AgentCore MCP server has also been tested to work on Claude Code, Amazon Q CLI, Cursor, and the VS Code Q plugin. First, let’s take a look at a typical agent development lifecycle using AgentCore services (remember that this is only one example with the tools available, and you are free to explore more such use cases simply by instructing the agent in your favorite Agentic IDE):

The agent development lifecycle follows these steps:

The user takes a local set of tools or MCP servers and
Creates a lambda target for AgentCore Gateway; or Deploys the MCP server as-is on AgentCore Runtime
The user prepares the actual agent code using a preferred framework like Strands Agents or LangGraph. The user can either:
Start from scratch (the server can fetch docs from the Strands Agents or LangGraph documentation) Start from fully or partially working agent code
The user asks the agent to transform the code into a format compatible with AgentCore Runtime with the intention to deploy the agent later. This causes the agent to:
Write an appropriate requirements.txt file import necessary libraries including bedrock_agentcore decorate the main handler (or create one) to access the core agent calling logic or input handler
The user may then ask the agent to deploy to AgentCore Runtime. The agent can look up documentation and can use the AgentCore CLI to deploy the agent code to Runtime The user can test the agent by asking the agent to do so. The AgentCore CLI command required for this is written and executed by the client The user then asks to modify the code to use the deployed AgentCore Gateway MCP server within this AgentCore Runtime agent.
The agent modifies the original code to add an MCP client that can call the deployed gateway The agent then deploys a new version v2 of the agent to Runtime The agent then tests this integration with a new prompt

Here is a demo of the MCP server working with Cursor IDE. We see the agent perform the following steps:

Transform the weather_agent.py to be compatible with AgentCore runtime Use the AgentCore CLI to deploy the agent Test the deployed agent with a successful prompt