AWS Open-Sources Bedrock AgentCore MCP Server to Turn IDE Prompts into Deployable Agents

AWS open-sources an MCP server for Bedrock AgentCore: ship agents from your IDE chat

AWS released an open-source Model Context Protocol (MCP) server for Amazon Bedrock AgentCore. It gives IDE assistants a direct path from natural-language prompts to deployable agents on AgentCore Runtime.

The server compresses refactor, environment setup, Gateway wiring, deployment, and testing into guided chat steps. Result: fewer CLI hops, less glue code, faster feedback.

What it is

The AgentCore MCP server exposes task-specific tools to MCP clients like Kiro, Claude Code, Cursor, Amazon Q Developer CLI, and the VS Code Q plugin. From the chat surface, your assistant can:

• Refactor an existing agent to the AgentCore Runtime model with minimal edits
• Provision and configure AWS (credentials, roles/permissions, ECR, config files)
• Wire up AgentCore Gateway for tool calls
• Deploy, invoke, and test the agent end-to-end

What it does in your codebase

• Converts entry points to AgentCore handlers
• Adds bedrock_agentcore imports and generates a requirements.txt
• Rewrites direct agent calls into payload-based handlers compatible with Runtime
• Invokes the AgentCore CLI to deploy and exercise the agent, including Gateway tool paths

Install and client support

Setup is a one-click flow from the GitHub repo using a lightweight launcher (uvx) and a standard mcp.json entry. Most MCP-capable clients will pick it up automatically.

• Expected mcp.json locations:
  • Kiro: .kiro/settings/mcp.json
  • Cursor: .cursor/mcp.json
  • Amazon Q CLI: ~/.aws/amazonq/mcp.json
  • Claude Code: ~/.claude/mcp.json
• Repository: awslabs "mcp" mono-repo (Apache-2.0)

Architecture: layered context that actually helps

AWS recommends a layered context model so your IDE assistant plans the whole transform→deploy→test loop without manual context switching:

• Start with the agentic client (your IDE assistant)
• Add the AWS Documentation MCP Server
• Layer in framework docs (Strands Agents, LangGraph)
• Include AgentCore and agent-framework SDK docs
• Guide repetitive moves via per-IDE "steering files"

This reduces retrieval misses and keeps the assistant grounded across code, infra, and deployment steps.

Typical developer workflow

• Bootstrap: Use local tools or MCP servers. Provision a Lambda target for AgentCore Gateway or deploy directly to AgentCore Runtime.
• Author/Refactor: Start from Strands Agents or LangGraph. Convert handlers, imports, and dependencies for Runtime compatibility.
• Deploy: Use the AgentCore CLI from the assistant's toolcalls.
• Test & iterate: Invoke in natural language. If tools are needed, integrate Gateway (MCP client inside the agent), redeploy (v2), retest.

Why this matters

Most agent frameworks pull you into cloud plumbing first: credentials, roles, registries, CLIs. This server offloads that to the IDE assistant and narrows the prompt-to-production gap.

Because it's "just" another MCP server, it composes cleanly with existing doc servers and frameworks. Teams standardizing on Bedrock AgentCore get a low-friction entry point and a repeatable workflow instead of ad-hoc scripts.

Quick checklist to try it

• Install via uvx and register the server in mcp.json for your IDE client
• Point the assistant at your current agent repo (Strands/LangGraph welcomed)
• Let the assistant apply handler conversions and add bedrock_agentcore dependencies
• Provision AWS roles, credentials, and ECR via the server's tools
• Deploy with the AgentCore CLI, wire Gateway if needed, run an end-to-end test

Useful links

• AWS blog: Accelerate development with the AgentCore MCP server
• GitHub: awslabs/mcp mono-repo

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