Common Use Cases and Patterns

This document provides practical examples of how to use the Atlantis MCP Server for common development scenarios.

Table of Contents

Starting a New Project

Use Case: Bootstrap a New Serverless API

Goal: Create a new serverless API project using Atlantis conventions.

Steps:

  1. Find Appropriate Starter Code

    Ask AI: "Show me available application starters for Node.js"

    Response includes starters with language, framework, and features.

  2. Get Starter Details

    Ask AI: "Tell me about atlantis-starter-02"

    Review prerequisites, features, and example code.

  3. Identify Required Templates

    Ask AI: "What templates do I need for a serverless API?"

    AI suggests:

  4. Validate Project Naming

    Ask AI: "Validate these names:
- acme-person-api-test-GetPersonFunction
- acme-person-api-test-PersonTable
- acme-person-api-test-ApiGateway"

    AI validates each name and provides feedback.

  5. Get Template Content

    Ask AI: "Get template-pipeline.yml and explain the parameters"

    AI provides template with parameter descriptions.

Expected Outcome: Complete project structure with validated naming and appropriate templates.

Adding Infrastructure

Use Case: Add DynamoDB Caching to Existing Application

Goal: Implement caching using DynamoDB and cache-data package.

Steps:

  1. Search for Caching Documentation

    Ask AI: "Search documentation for DynamoDB caching patterns"

    AI returns relevant documentation and code examples.

  2. Find Storage Template

    Ask AI: "Show me storage templates for DynamoDB"

    AI lists available DynamoDB templates.

  3. Get Template Details

    Ask AI: "Get template-storage-dynamodb-cache.yml"

    AI provides template with parameters and outputs.

  4. Find Code Examples

    Ask AI: "Find code examples for cache-data package"

    AI provides implementation examples.

  5. Validate New Resources

    Ask AI: "Validate: acme-myapp-test-CacheTable"

    AI confirms naming is correct.

Expected Outcome: DynamoDB table template and implementation code following best practices.

Updating Existing Templates

Use Case: Update Pipeline Template to Latest Version

Goal: Safely update template-pipeline.yml from v2.0.17 to latest.

Steps:

  1. Check for Updates

    Ask AI: "Check if template-pipeline.yml v2.0.17 has updates"

    AI reports:

  2. Review Changelog

    Ask AI: "Show me the full changelog for template-pipeline.yml v2.0.18"

    AI provides detailed changes:

  3. Get New Version

    Ask AI: "Get template-pipeline.yml version v2.0.18"

    AI provides updated template content.

  4. Compare Versions

    Ask AI: "What are the differences between v2.0.17 and v2.0.18?"

    AI highlights key changes.

  5. Verify Compatibility

    Ask AI: "Are there any breaking changes in v2.0.18?"

    AI confirms no breaking changes.

Expected Outcome: Safe template update with full understanding of changes.

Learning Best Practices

Use Case: Learn Lambda Function Best Practices

Goal: Understand and implement Lambda best practices using Atlantis patterns.

Steps:

  1. Search Documentation

    Ask AI: "Search documentation for Lambda function best practices"

    AI returns guides and tutorials.

  2. Find Code Examples

    Ask AI: "Show me Lambda function examples from starters"

    AI provides code snippets with explanations.

  3. Learn Caching Patterns

    Ask AI: "How do I implement caching in Lambda functions?"

    AI explains cache-data integration.

  4. Understand Error Handling

    Ask AI: "What are error handling patterns for Lambda?"

    AI provides error handling examples.

  5. Review Naming Conventions

    Ask AI: "What are the naming conventions for Lambda functions?"

    AI explains: Prefix-ProjectId-StageId-FunctionName

Expected Outcome: Comprehensive understanding of Lambda best practices with code examples.

Validating Resources

Use Case: Validate All Resources Before Deployment

Goal: Ensure all resource names follow Atlantis conventions before deploying.

Steps:

  1. Validate Lambda Functions

    Ask AI: "Validate these Lambda function names:
- acme-myapp-test-GetUserFunction
- acme-myapp-test-CreateUserFunction
- acme-myapp-test-DeleteUserFunction"

    AI validates each name.

  2. Validate DynamoDB Tables

    Ask AI: "Validate: acme-myapp-test-UsersTable"

    AI confirms valid.

  3. Validate S3 Buckets

    Ask AI: "Is this S3 bucket name valid: 
acme-myapp-test-us-east-1-123456789012"

    AI validates S3 bucket naming pattern.

  4. Validate CloudFormation Stack

    Ask AI: "Validate: acme-myapp-test-ApiStack"

    AI confirms valid.

  5. Get Naming Suggestions

    Ask AI: "I have a function that processes orders. What should I name it?"

    AI suggests: acme-myapp-test-ProcessOrdersFunction

Expected Outcome: All resources validated before deployment, preventing naming errors.

Exploring Documentation

Use Case: Find Implementation Guidance for New Feature

Goal: Implement CloudFront caching for S3-hosted website.

Steps:

  1. Search for CloudFront Documentation

    Ask AI: "Search documentation for CloudFront caching"

    AI returns relevant guides.

  2. Find Template Patterns

    Ask AI: "Show me CloudFormation patterns for CloudFront"

    AI provides template examples.

  3. Search for Code Examples

    Ask AI: "Find code examples for CloudFront invalidation"

    AI provides Lambda function examples.

  4. Find Related Templates

    Ask AI: "What templates do I need for CloudFront with S3?"

    AI suggests:

  5. Get Implementation Guide

    Ask AI: "How do I set up CloudFront with S3 origin?"

    AI provides step-by-step guide.

Expected Outcome: Complete implementation guidance with templates and code examples.

Working with Multiple Environments

Use Case: Deploy to Test, Beta, and Production

Goal: Manage templates and naming across multiple environments.

Steps:

  1. Validate Test Environment Names

    Ask AI: "Validate these test environment names:
- acme-myapp-test-ApiFunction
- acme-myapp-test-DataTable"

    AI confirms valid.

  2. Validate Beta Environment Names

    Ask AI: "Validate these beta environment names:
- acme-myapp-beta-ApiFunction
- acme-myapp-beta-DataTable"

    AI confirms valid.

  3. Validate Production Environment Names

    Ask AI: "Validate these production environment names:
- acme-myapp-prod-ApiFunction
- acme-myapp-prod-DataTable"

    AI confirms valid.

  4. Get Environment-Specific Templates

    Ask AI: "What templates should I use for production vs test?"

    AI explains differences:

  5. Check Template Versions Across Environments

    Ask AI: "I'm using template-pipeline.yml v2.0.17 in test and v2.0.16 in prod. Should I update prod?"

    AI checks for updates and breaking changes.

Expected Outcome: Consistent naming and appropriate templates across all environments.

Team Collaboration

Use Case: Onboard New Team Member

Goal: Help new developer understand Atlantis conventions and available resources.

Steps:

  1. Introduce Available Resources

    Ask AI: "What CloudFormation templates are available?"

    AI lists all templates by category.

  2. Explain Naming Conventions

    Ask AI: "Explain Atlantis naming conventions"

    AI provides detailed explanation with examples.

  3. Show Starter Code

    Ask AI: "What application starters are available?"

    AI lists starters with descriptions.

  4. Provide Documentation Links

    Ask AI: "Where can I find documentation for cache-data?"

    AI provides documentation links and examples.

  5. Validate Example Names

    Ask AI: "Validate this name I created: myapp-function-test"

    AI identifies issues and suggests: acme-myapp-test-MyFunction

Expected Outcome: New team member understands conventions and can find resources independently.

Advanced Patterns

Pattern 1: Multi-Bucket Template Discovery

Scenario: Organization uses multiple S3 buckets for different teams.

Ask AI: "Show me templates from the finance bucket"

AI filters results to specific bucket.

Pattern 2: Version Comparison

Scenario: Need to understand changes between versions.

Ask AI: "Compare template-storage.yml v1.3.5 and v2.0.0"

AI highlights differences and breaking changes.

Pattern 3: Bulk Validation

Scenario: Validate all resources in a CloudFormation template.

Ask AI: "Validate all resource names in this template:
[paste template content]"

AI validates each resource and reports issues.

Pattern 4: Documentation Search with Filters

Scenario: Find specific type of documentation.

Ask AI: "Find troubleshooting guides for Lambda functions"

AI filters by documentation type.

Pattern 5: Starter Code Comparison

Scenario: Choose between multiple starters.

Ask AI: "Compare atlantis-starter-01 and atlantis-starter-02"

AI highlights differences in features and use cases.

Tips for Effective Use

1. Be Specific

Instead of: “Show me templates” Use: “Show me Pipeline templates version v2.0.18”

2. Use Filters

Leverage filtering capabilities: - By category - By version - By bucket - By organization

3. Validate Early

Check naming before writing code:

Ask AI: "Validate: acme-myapp-test-MyFunction"

4. Search Documentation First

Before asking general questions:

Ask AI: "Search documentation for [topic]"

5. Check for Updates Regularly

Stay current:

Ask AI: "Check all my templates for updates"

6. Leverage Code Examples

Learn by example:

Ask AI: "Show me code examples for [feature]"

Support

If you need help with a specific use case: