What is this 'MCP' thing AI experts are talking about?

Update, October 2026: MCP has grown a great deal since this was written in March 2025, and the server count and examples below are out of date. Our guide to what MCP is and how businesses use it has current figures, who now governs the standard, and the risks to manage.

Think of it as a "USB for AI" – a common interface that lets any AI model seamlessly access databases, apps, APIs, and other systems without custom coding.

What MCP Does

• Universal Tool Access: Allows AI to dynamically discover and use external resources like Google Drive, Slack, or coding environments through standardized connections
• Automatic Integration: AI agents automatically detect available tools via MCP servers – no manual coding required (e.g., new CRM system appears as available tool immediately
• Vendor Agnostic: Works with any AI model (Claude, GPT-4, open-source LLMs) and any service that implements MCP

Why It Matters for Business

• Reduces Integration Costs: Instead of building custom connections for each tool, companies use pre-built MCP servers (1,000+ available as of 2025)
• Enables Complex Workflows: AI can chain actions across multiple systems – email a client update spreadsheet create Jira ticket, all through MCP
• Future-Proofs AI Investments: Adopted by major tools like Replit, Codeium, and IDEs, MCP ensures AI capabilities grow as new tools join the ecosystem

Real-World Impact

Companies like Block (Square) use MCP to connect financial data systems, while design teams leverage it for AI-powered asset generation directly from Figma files[3][6]. This shifts AI from isolated chatbots to fully integrated business assistants that understand your specific tools and data context.

The protocol is particularly valuable for:

• Non-technical users: Claude Desktop app lets anyone use MCP-powered tools through natural language
• Cross-platform automation: Mix AI providers (Claude + open-source models) while maintaining tool integrations
• Rapid prototyping: Test workflows by asking AI to execute sequences in tools like Unity/Blender without manual setup

MCP represents a fundamental shift in AI architecture – rather than just improving model intelligence, it focuses on connecting that intelligence to the real-world systems where work actually happens.