How MCP Servers Can Help Provide Better Context with Amazon Q CLI

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In modern software development, context is everything—especially when working with AI-driven coding assistants like Amazon Q CLI, which leverages generative AI to assist developers from the command line. To improve the quality, accuracy, and relevance of AI interactions, Model Context Protocol (MCP) servers act as a powerful middleware layer, enriching requests with historical, application-specific, and user-defined context.

In this article, we’ll explore how MCP servers function, why they’re essential for improving Amazon Q CLI performance, and how to implement one using a simple REST API. We’ll also walk through a working example that demonstrates how better context management improves productivity and output quality.

Understanding Amazon Q CLI

Amazon Q CLI is an AI-powered developer assistant integrated directly into your terminal. It uses generative AI to:

  • Answer code-related queries.

  • Generate and refactor code.

  • Debug and explain terminal output.

  • Automate mundane CLI tasks.

Unlike its IDE counterpart, Q CLI operates without full visibility into your editor or project layout unless explicitly informed. That’s where MCP comes in.

What is MCP (Model Context Protocol)?

The Model Context Protocol (MCP) is an open standard developed by AWS for enriching prompts and outputs of AI assistants by injecting structured context. This context includes:

  • File system structure

  • Code files and snippets

  • Session history

  • Environment metadata

  • Custom application signals

MCP provides a protocol-based abstraction between the model (Amazon Q or others) and the real-world development environment. The protocol works through an MCP server that implements defined endpoints and communicates with Amazon Q CLI during prompt execution.

Why MCP Servers Matter for Contextual Relevance

By default, Amazon Q CLI operates with limited visibility. It doesn’t automatically understand your app’s architecture, business rules, or external dependencies unless it’s explicitly told. MCP servers solve this by:

  • Dynamically providing relevant files and metadata.

  • Enabling AI to reason over project-specific data.

  • Automatically augmenting prompts with recent changes.

  • Allowing fine-grained customization of what context gets shared.

This results in higher quality completions, reduced hallucinations, and faster resolution of coding tasks.

Architecture of an MCP Server + Q CLI Integration

Let’s look at a high-level diagram of how this works:

pgsql
+-----------------+ Prompts +---------------------+
| Amazon Q CLI | <--------------> | MCP Server |
+-----------------+ +---------------------+
| |
v v
Terminal Input Local Project Context (FS, Git, Env)

  • The developer sends a query through Q CLI.

  • Q CLI invokes the MCP server’s endpoint.

  • MCP server responds with rich context.

  • Q CLI injects the context into the prompt sent to Amazon Q.

Implementing a Minimal MCP Server in Node.js

Here’s how you can write a minimal MCP server in Node.js (Express) to work with Amazon Q CLI.

Install Dependencies

bash
npm init -y
npm install express body-parser

Basic Server Setup

Create a file named server.js:

js
const express = require('express');
const bodyParser = require('body-parser');
const fs = require('fs');
const app = express();
const port = 4242;app.use(bodyParser.json());app.get(‘/mcp/context’, async (req, res) => {
const files = getRelevantFiles();
const envInfo = getEnvironmentMetadata();const context = {
files,
environment: envInfo,
toolVersion: “1.0.0”,
tags: [“nodejs”, “microservice”, “express”]
};res.json(context);
});function getRelevantFiles() {
const fileList = [‘server.js’, ‘package.json’];
return fileList.map(filename => ({
path: filename,
content: fs.readFileSync(filename, ‘utf-8’)
}));
}function getEnvironmentMetadata() {
return {
nodeVersion: process.version,
os: process.platform,
user: process.env.USER || process.env.USERNAME
};
}app.listen(port, () => {
console.log(`MCP server running on http://localhost:${port}`);
});

Run the Server

bash
node server.js

This server will respond to Q CLI with file contents and environment details—effectively customizing its output based on your project setup.

Connecting Q CLI to Your MCP Server

To connect the Amazon Q CLI to your MCP server:

  1. Start your MCP server on http://localhost:4242.

  2. Add the following configuration in your terminal:

bash
export AWSQ_MCP_ENDPOINT=http://localhost:4242/mcp/context

  1. Now invoke Q CLI:

bash
q ask "Explain what this Express server does."

Q CLI will query your MCP server before generating a response, allowing Amazon Q to produce highly tailored, project-aware completions.

With vs Without MCP

Let’s examine how the same query behaves with and without MCP context.

Query:

bash
q ask "Can you help me refactor the Express server to support async/await properly?"

Output WITHOUT MCP:

“Sure! You can use async functions in Express routes. However, since I don’t have your code, here’s a generic example…”

💡 This is vague and requires manual alignment with your actual code.

Output WITH MCP:

“I see your getRelevantFiles() and getEnvironmentMetadata() functions use sync file reads. You could refactor them as follows…”
(Gives tailored code suggestion based on your actual files.)

✅ Now the suggestion is specific, actionable, and relevant.

Advanced MCP Customization Tips

To further improve results:

  • Tag your project type (e.g., ["nextjs", "monorepo", "graphql"]).

  • Add Git commit history or diff to show recent changes.

  • Include project README.md for goals, constraints, or standards.

  • Use caching so your MCP server doesn’t read from disk every time.

You could even build a dynamic context collector that watches your filesystem or Git events and updates the context cache in real-time.

Security Considerations

Since MCP servers expose local files to a querying agent, keep the following in mind:

  • Restrict to localhost only.

  • Validate allowed file paths.

  • Sanitize environment metadata.

  • Avoid exposing credentials or secrets.

  • Set up authentication if needed for team-wide deployments.

Using MCP in Team Environments

For team projects:

  • Deploy MCP servers as containers.

  • Serve multiple repositories with routing.

  • Integrate with your CI/CD pipelines to reflect recent builds or test logs.

  • Add observability to track how context is used and tune responses.

Best Practices for MCP + Amazon Q CLI

Best Practice Description
Minimal context Avoid overloading with too much file data.
Use tags Helps guide prompt behavior (e.g., ["typescript", "frontend"]).
Monitor responses Log input-output pairs to improve future context logic.
Include tests MCP can expose your test files for better test-driven completions.

In the age of generative AI tooling, context is the key differentiator between useful and transformative developer experiences. Amazon Q CLI already provides powerful terminal-based assistance, but its true potential is unlocked when integrated with a well-designed MCP server.

By supplying rich, relevant, and dynamic context—ranging from source code and environment details to project-specific metadata—MCP servers empower Q CLI to deliver smarter suggestions, write higher-quality code, and reduce the need for follow-up clarification.

Whether you’re a solo developer automating tasks or a team building production-grade services, adding an MCP layer to your workflow is a low-effort, high-impact improvement. It bridges the gap between static prompts and real-world projects, making AI an actual teammate rather than just a tool.

Start small with a local MCP server. Over time, evolve it into a custom context layer that adapts to your team, tooling, and workflows. With Amazon Q CLI and MCP working together, the future of context-aware software development is already here.