The Four Core Approaches to AI Connectors: Model Context Protocol (MCP) Servers

3. Model Context Protocol (MCP) Servers

The Approach: Deploy standardized MCP server instances that expose your business systems through an open protocol that AI systems can discover and use.

While MCP is newer, early adopters are building servers for systems like Postgres databases (enabling AI to query directly), Google Drive (for document access), Slack (for team communication), GitHub (for code operations), and internal APIs wrapped in MCP format. Anthropic has published reference implementations for common platforms like Brave Search, Fetch, and Memory systems.

How it actually works in practice:

You deploy an MCP server that sits in front of your customer data platform. The server exposes "resources" (customer profiles, purchase history), "tools" (update customer preferences, trigger email campaigns), and "prompts" (common query templates). Your AI agent connects to this server, discovers what's available through metadata, and makes requests in standardized MCP format. The server translates these into your CDP's native API calls.

Real-world example: A SaaS company building an MCP server for their internal analytics warehouse that allows their AI product assistant to query user behavior data, segment definitions, and feature adoption metrics—with the same server potentially used by multiple AI agents across different departments.

Advantages

• Standardized protocol works across different AI models and vendors
• Compose multiple MCP servers for different capabilities
• Open source foundation reduces vendor lock-in risks
• AI agents dynamically discover available tools at runtime
• Security isolation with servers as controlled access points
• Future-proof as more AI systems adopt the standard

Challenges

• Limited production case studies and enterprise best practices
• Must provision and maintain server infrastructure
• Smaller ecosystem of pre-built connectors compared to iPaaS
• Teams need to learn new protocol specifications and patterns
• Still requires development work for proprietary system connections
• Enterprise monitoring and observability tools still maturing

The critical question for your team: Are you building AI capabilities that will outlast any single vendor's platform, or solving for immediate business needs? Let's discuss this at length.

Read the previous posts: Part 1: API-Based Connectors | Part 2: Integration Platform as a Service (iPaaS)