AWS launched an open-source Mannequin 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 bundle 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 recordsdata); (3) wire up AgentCore Gateway for software calls; and (4) invoke and check the deployed agent—all from the IDE’s chat floor.
Virtually, the server teaches your coding assistant to transform entry factors to AgentCore handlers, add bedrock_agentcore imports, generate necessities.txt, and rewrite direct agent calls into payload-based handlers suitable with Runtime. It may well then name the AgentCore CLI to deploy and train the agent, together with end-to-end calls by means of Gateway instruments.
The best way to Set up? and what’s the shopper help?
AWS supplies a one-click set up stream from the GitHub repository, utilizing a light-weight launcher (uvx) and a regular mcp.json entry that almost all MCP-capable purchasers eat. The AWS staff lists the anticipated mcp.json areas 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). Whereas the AgentCore server listing hosts the implementation, the basis repo additionally hyperlinks to broader AWS MCP sources and documentation.
Structure steering and the “layered” context mannequin
AWS recommends a layered method to present 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 Brokers, LangGraph), embrace the AgentCore and agent-framework SDK docs, and eventually steer recurrent workflows through per-IDE “steering recordsdata.” This association reduces retrieval misses and helps the assistant plan the end-to-end rework/deploy/check loop with out handbook context switching.
Growth workflow (typical path)
- Bootstrap: Use native instruments or MCP servers. Both provision a Lambda goal for AgentCore Gateway or deploy the server on to AgentCore Runtime.
- Writer/Refactor: Begin from Strands Brokers or LangGraph code. The server instructs the assistant to transform handlers, imports, and dependencies for Runtime compatibility.
- Deploy: The assistant appears up related docs and invokes the AgentCore CLI to deploy.
- Check & Iterate: Invoke the agent through pure language; if instruments are wanted, combine Gateway (MCP shopper contained in the agent), redeploy (v2), and retest.
How does it make a distinction?
Most “agent frameworks” nonetheless require builders to study 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. Because 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.
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/Reminiscence stack whereas preserving current Strands/LangGraph code paths. Virtually, this collapses the immediate→refactor→deploy→check loop right into a reproducible, scriptable workflow somewhat than bespoke glue code.
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its reputation amongst audiences.
