Streamline AI Agent Development with AWS’s New MCP Server for Bedrock AgentCore
AWS recently introduced an open-source Model Context Protocol (MCP) server for Amazon Bedrock AgentCore, aiming to simplify the integration of natural-language prompts into deployable agents. This innovative solution offers a more efficient path from coding to deployment with the help of automated transformations and tooling hooks.
What is the MCP Server?
The AgentCore MCP server is designed to connect task-specific tools with various clients, including Kiro, Claude Code, and VS Code plugins. It facilitates essential tasks such as:
- Refactoring an existing agent to fit the AgentCore Runtime model.
- Configuring the AWS environment, including credentials and permissions.
- Integrating the AgentCore Gateway for tool calls.
- Testing and invoking deployed agents directly from the IDE.
This functionality allows your coding assistant to convert entry points to AgentCore handlers effortlessly, enabling direct calls to the AgentCore CLI to deploy and test agents efficiently.
How to Install and Client Support
To install the MCP server, AWS offers a straightforward one-click solution via their GitHub repository. Users simply need the lightweight launcher, uvx, and the standard mcp.json file that most MCP-compatible clients will automatically recognize.
The expected locations for mcp.json files include:
- Kiro:
.kiro/settings/mcp.json - Cursor:
.cursor/mcp.json - Amazon Q CLI:
~/.aws/amazonq/mcp.json - Claude Code:
~/.claude/mcp.json
Architectural Guidance and Layered Context Model
AWS advocates a layered context approach to enhance the IDE assistant’s capabilities. The recommended structure includes:
- Starting from the agentic client.
- Adding AWS Documentation MCP Server.
- Including framework documentation such as Strands Agents and LangGraph.
- Steering recurrent workflows through per-IDE “steering files.”
This method minimizes context-switching and facilitates a more seamless development process.
Typical Development Workflow
The development cycle with the MCP server typically follows these steps:
- Bootstrap: Utilize local tools or MCP servers to set up a Lambda target or deploy the server directly.
- Author/Refactor: Begin with Strands Agents or LangGraph code, guiding the assistant to refactor appropriately.
- Deploy: Use the AgentCore CLI for deployment after referencing necessary documentation.
- Test & Iterate: Call the agent via natural language, integrating the Gateway if needed, and redeploy as necessary.
How Does the MCP Server Make a Difference?
Traditional agent frameworks often require developers to learn complex cloud-specific tools and policies before making substantial progress. In contrast, the AWS MCP server shifts much of this workload into the IDE assistant, effectively bridging the “prompt-to-production” gap. This integration not only simplifies the deployment process but also ensures continuity with existing documentation and frameworks.
In summary, the AWS MCP server for Bedrock AgentCore represents a significant advancement in AI agent development, making it easier for teams to leverage cloud technology without the steep learning curve.
Conclusion
With the launch of the MCP server, AWS is streamlining the workflow of AI agent development, enabling a more efficient transition from coding to deployment. This innovative tool is essential for developers looking to maximize their productivity within the AWS ecosystem.
Keywords: AWS Bedrock, AgentCore MCP Server, AI Agent Development, Cloud Integration, Model Context Protocol, Development Tools, Natural Language Processing.
