MCP encourages a modular, composable architecture for AI integrations:

• Lightweight Services: Each MCP server focuses on a specific capability or data source
• Mix and Match: Combine multiple servers to build complex, multi-capability workflows
• Reusable Components: Build against the standard once, reuse across many integrations
• Lego-Like Assembly: Snap together different capabilities as needed for each use case

In Practice

An AI agent could simultaneously connect to:

• A Slack MCP server for messaging capabilities
• A database MCP server for data queries
• A workflow automation MCP server for complex business processes
• A document repository for content retrieval

This modularity significantly reduces development effort and promotes more maintainable AI systems.