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Getting Started with Amazon Q CLI and AWS MCP Servers on Windows
If you're curious about how to bring generative AI to your terminal and enhance it with contextual awareness of AWS services, this blog walks you through setting up Amazon Q CLI on a Windows environment and connecting it to one or more MCP (Model Context Protocol) servers. Whether you're experimenting with infrastructure as code, checking AWS best practices, or planning cost analysis, MCP gives Amazon Q the power to understand your environment better. What’s Amazon Q CLI and MCP? Amazon Q CLI is a command-line tool that lets you interact with a generative AI assistant directly in your terminal. Think of it as having a smart AWS-AI assistant that can help with commands, code generation, documentation lookup, and much more. MCP (Model Context Protocol) is an open protocol that connects AI tools like Q CLI to external services — such as AWS documentation, CDK best practices, cost calculators, or even image generators. MCP servers provide that real-time context the model might not have, especially for newer APIs or service updates.
If you're curious about how to bring generative AI to your terminal and enhance it with contextual awareness of AWS services, this blog walks you through setting up Amazon Q CLI on a Windows environment and connecting it to one or more MCP (Model Context Protocol) servers.
Whether you're experimenting with infrastructure as code, checking AWS best practices, or planning cost analysis, MCP gives Amazon Q the power to understand your environment better.
What’s Amazon Q CLI and MCP?
Amazon Q CLI is a command-line tool that lets you interact with a generative AI assistant directly in your terminal. Think of it as having a smart AWS-AI assistant that can help with commands, code generation, documentation lookup, and much more.
MCP (Model Context Protocol) is an open protocol that connects AI tools like Q CLI to external services — such as AWS documentation, CDK best practices, cost calculators, or even image generators. MCP servers provide that real-time context the model might not have, especially for newer APIs or service updates.
