|

AWS Open-Sources an MCP Server for Bedrock AgentCore to Streamline AI Agent Development

ByRicardo

AWS launched an open-source Model Context Protocol (MCP) server for Amazon Bedrock AgentCore, offering a direct path from natural-language prompts in agentic IDEs to deployable brokers on AgentCore Runtime. The package deal ships with automated transformations, atmosphere provisioning, and Gateway/tooling hooks designed to compress typical multi-step integration work into conversational instructions.

So, what precisely is it?

The “AgentCore MCP server” exposes task-specific instruments to a shopper (e.g., Kiro, Claude Code, Cursor, Amazon Q Developer CLI, or the VS Code Q plugin) and guides the assistant to: (1) minimally refactor an current agent to the AgentCore Runtime mannequin; (2) provision and configure the AWS atmosphere (credentials, roles/permissions, ECR, config information); (3) wire up AgentCore Gateway for instrument calls; and (4) invoke and take a look at the deployed agent—all from the IDE’s chat floor.

Practically, the server teaches your coding assistant to convert entry factors to AgentCore handlers, add bedrock_agentcore imports, generate necessities.txt, and rewrite direct agent calls into payload-based handlers appropriate with Runtime. It can then name the AgentCore CLI to deploy and train the agent, together with end-to-end calls by Gateway instruments.

https://aws.amazon.com/blogs/machine-learning/accelerate-development-with-the-amazon-bedrock-agentcore-mcpserver/

How to Install? and what’s the shopper assist?

AWS gives a one-click set up movement from the GitHub repository, utilizing a light-weight launcher (uvx) and a typical mcp.json entry that almost all MCP-capable purchasers eat. The AWS team lists the anticipated mcp.json places for Kiro (.kiro/settings/mcp.json), Cursor (.cursor/mcp.json), Amazon Q CLI (~/.aws/amazonq/mcp.json), and Claude Code (~/.claude/mcp.json).

The repository sits within the awslabs “mcp” mono-repo (license Apache-2.0). While the AgentCore server listing hosts the implementation, the foundation repo additionally hyperlinks to broader AWS MCP assets and documentation.

Architecture steerage and the “layered” context mannequin

AWS recommends a layered strategy to give the IDE’s assistant progressively richer context: begin with the agentic shopper, then add the AWS Documentation MCP Server, layer in framework documentation (e.g., Strands Agents, LangGraph), embrace the AgentCore and agent-framework SDK docs, and at last steer recurrent workflows through per-IDE “steering information.” This association reduces retrieval misses and helps the assistant plan the end-to-end rework/deploy/take a look at loop with out guide context switching.

Development workflow (typical path)

  1. Bootstrap: Use native instruments or MCP servers. Either provision a Lambda goal for AgentCore Gateway or deploy the server instantly to AgentCore Runtime.
  2. Author/Refactor: Start from Strands Agents or LangGraph code. The server instructs the assistant to convert handlers, imports, and dependencies for Runtime compatibility.
  3. Deploy: The assistant seems up related docs and invokes the AgentCore CLI to deploy.
  4. Test & Iterate: Invoke the agent through pure language; if instruments are wanted, combine Gateway (MCP shopper contained in the agent), redeploy (v2), and retest.
https://aws.amazon.com/blogs/machine-learning/accelerate-development-with-the-amazon-bedrock-agentcore-mcpserver/

How does it make a distinction?

Most “agent frameworks” nonetheless require builders to be taught cloud-specific runtimes, credentials, position insurance policies, registries, and deployment CLIs earlier than any helpful iteration. AWS’s MCP server shifts that work into the IDE assistant and narrows the “prompt-to-production” hole. Since it’s simply one other MCP server, it composes with current doc servers (AWS service docs, Strands, LangGraph) and might experience enhancements in MCP-aware purchasers, making it a low-friction entry level for groups standardizing on Bedrock AgentCore.

Comments from MTP (Marktechpost group)

I like that AWS shipped an actual MCP endpoint for AgentCore that my IDE can name instantly. The uvx-based mcp.json config makes shopper hookup trivial (Cursor, Claude Code, Kiro, Amazon Q CLI), and the server’s tooling maps cleanly onto the AgentCore Runtime/Gateway/Memory stack whereas preserving current Strands/LangGraph code paths. Practically, this collapses the immediate→refactor→deploy→take a look at loop right into a reproducible, scriptable workflow slightly than bespoke glue code.

Check out the GitHub Repo and Technical details. Feel free to take a look at our GitHub Page for Tutorials, Codes and Notebooks. Also, be at liberty to observe us on Twitter and don’t neglect to be a part of our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.

The submit AWS Open-Sources an MCP Server for Bedrock AgentCore to Streamline AI Agent Development appeared first on MarkTechPost.

Similar Posts