AI Integration

The MBC CQRS Serverless framework is designed with AI tool integration in mind.

Overview

Modern development workflows increasingly include AI assistance for code generation, debugging, and documentation lookup. This framework supports AI development tools through llms.txt files and MCP server.

llms.txt Convention

What is llms.txt?

The llms.txt convention provides a standardized way for websites and projects to expose information for LLMs.

File Structure

The framework provides two versions:

• llms.txt - Concise overview and quick reference
• llms-full.txt - Comprehensive documentation and context

Using llms.txt

AI tools can directly fetch these files to build context about the framework:

# Short version for quick context
curl https://mbc-cqrs-serverless.mbc-net.com/llms.txt

# Full version for comprehensive context
curl https://mbc-cqrs-serverless.mbc-net.com/llms-full.txt

MCP Server Integration

The Model Context Protocol (MCP) server provides a more dynamic way for AI tools to interact with the framework.

Context7 Integration

Context7 is an MCP server that provides real-time, version-specific documentation to AI assistants.

MBC CQRS Serverless documentation is available through Context7. You can access it at:

• Context7 URL: https://context7.com/mbc-net/mbc-cqrs-serverless-doc

To use Context7, add it to Claude Code:

claude mcp add context7 -- npx -y @upstash/context7-mcp@latest

Custom MCP Server

The framework also provides a custom MCP server for deeper integration:

Feature	Description
Resources	Access to framework documentation
Tools	Code generation and validation tools
Prompts	Guided workflows for common tasks

Setup

Add to Claude Code or other MCP-compatible tools:

{
  "mcpServers": {
    "mbc-cqrs-serverless": {
      "command": "npx",
      "args": ["@mbc-cqrs-serverless/mcp-server"],
      "env": {
        "MBC_PROJECT_PATH": "/path/to/your/project"
      }
    }
  }
}

Learn more: MCP Server Documentation

Best Practices

Documentation First

Before tackling complex tasks, let AI read the framework documentation:

1. Use MCP resources to fetch architecture documentation
2. Review CQRS patterns and Event Sourcing concepts
3. Examine existing modules as reference patterns

Code Generation

When asking AI to generate modules, be specific:

"Generate an Order module with async command handling and validation"

Debugging Assistance

When encountering errors, AI can use the error catalog to find solutions:

"I'm getting error 'version mismatch'. What should I do?"

Supported Tools

The following AI tools can integrate with MBC CQRS Serverless:

Tool	Support	Notes
Claude Code	Full Support	Native MCP support
Cursor	Full Support	MCP support available
GitHub Copilot	Partial Support	Via llms.txt

Related Documentation

• AI Prompts Library - Optimized prompts for common tasks
• MCP Server - Detailed MCP server documentation
• CLI Tool - CLI commands for code generation
• Error Catalog - Error reference with solutions
• Architecture - System architecture overview