Speed Up AI Agent Development with Amazon Bedrock AgentCore MCP Server

Published Date : 3/10/2025 

Discover how the Amazon Bedrock AgentCore MCP Server simplifies and accelerates the development of AI agents, offering built-in support for runtime, gateway integration, identity management, and agent memory. 

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 the 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:

1. 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.

2. 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.

3. Simplifies tool integration with Bedrock AgentCore Gateway for seamless agent-to-tool communication in the cloud environment.

4. 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:

1. 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.

2. Layer 2: AWS service documentation – Install the AWS Documentation MCP Server for comprehensive AWS service documentation, including context about Bedrock AgentCore.

3. Layer 3: Framework documentation – Install the Strands, LangGraph, or other framework docs MCP servers or use the llms.txt for framework-specific context.

4. 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.

5. 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.

| Client | Location of mcp.json | Documentation |

|--------|----------------------|---------------|

| Kiro | .kiro/settings/mcp.json | https://kiro.dev/docs/mcp/ |

| Cursor | .cursor/mcp.json | https://cursor.com/docs/context/mcp |

| Q CLI | ~/.aws/amazonq/mcp.json | https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/qdev-mcp.html |

| Claude Code | ~/.claude/mcp.json | https://docs.claude.com/en/docs/claude-code/mcp |

Use the following in your mcp.json:

```json

{ 

Frequently Asked Questions (FAQS): 

Q: What is the Amazon Bedrock AgentCore MCP Server?

A: The Amazon Bedrock AgentCore MCP Server is a tool designed to simplify and accelerate the development of AI agents on AWS. It provides built-in support for runtime, gateway integration, identity management, and agent memory.

Q: How does the AgentCore MCP Server help with development?

A: The AgentCore MCP Server automates the setup and integration processes, transforming agents for compatibility with Bedrock AgentCore, and simplifying tool integration and testing.

Q: What are the key capabilities of the AgentCore MCP Server?

A: Key capabilities include transforming agents for AgentCore Runtime integration, automating development environment provisioning, simplifying tool integration with Bedrock AgentCore Gateway, and enabling simple agent invocation and testing.

Q: How do I install the AgentCore MCP Server?

A: You can install the AgentCore MCP Server using the one-click install on the Github repository. Each IDE integrates with the MCP differently, so refer to the IDE documentation for the location of the mcp.json file.

Q: What is the layered approach for using the AgentCore MCP Server?

A: The layered approach involves using agentic IDEs, AWS service documentation, framework documentation, SDK documentation, and steering files to provide comprehensive AI agent development support. 

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