What is the Model Context Protocol (MCP)
The Model Context Protocol — MCP — is an open protocol developed by Anthropic to standardize how AI models connect to external data sources and tools. Think of it as a universal 'USB port' for AI: a standard interface that allows Claude to connect to any business system.
Before MCP, every AI integration required custom development: specific APIs, custom adapters, connection logic written from scratch. MCP eliminates this complexity by offering a common protocol that works with any data source.
How MCP works: architecture and components
MCP is based on a client-server architecture. The MCP client is integrated into the AI application (Claude), while MCP servers are lightweight components that expose data and functionality from external systems.
An MCP server can expose three types of capabilities: Resources (readable data like files, database records, documents), Tools (executable actions like sending emails, creating tickets, updating CRM) and Prompts (predefined interaction templates for specific scenarios).
Communication happens via JSON-RPC 2.0, ensuring interoperability and implementation simplicity.
Why MCP is a game-changer for businesses
MCP solves the fundamental problem of enterprise AI: isolation. An AI model without access to business data is like a brilliant consultant locked in an empty room. MCP opens the door, allowing Claude to access real-time CRM, ERP, database, file system and any other system data.
This means Claude can answer questions based on actual company data, execute concrete actions in existing systems and maintain updated context without needing to manually upload documents.
Concrete MCP use cases
The practical applications of MCP are vast. In customer support, an MCP server connected to the CRM allows Claude to access customer history and provide personalized responses. In business intelligence, MCP enables Claude to query SQL databases and generate reports in natural language.
In process automation, Claude can use MCP to orchestrate actions across multiple systems: read an order from the management system, verify warehouse availability, update status and notify the customer — all in a single conversation.
In document management, MCP allows Claude to access SharePoint, Google Drive or internal document systems to search, analyze and synthesize documents.
Implementing MCP: practical considerations
Implementing MCP requires specific skills but isn't complex. Anthropic provides SDKs in Python and TypeScript for building MCP servers. Each server is typically a lightweight microservice that bridges Claude and a specific system.
Key considerations include: security (each MCP server must implement authentication and authorization), performance (calls must be efficient to avoid slowing interaction) and governance (it's important to control which data and actions are exposed to the AI).
Maverick AI has direct experience implementing MCP architectures for businesses, from server design to production deployment with monitoring and access control.