MCP: The Universal Adapter for AI
The Model Context Protocol gives AI agents a standardized way to connect to tools, data, and services — no custom glue code required.
What is MCP?
Model Context Protocol is a standard for connecting AI models to external tools and data. It's like a universal adapter — instead of writing custom code for every integration, MCP provides a standardized interface.
Think of it as USB for AI. Before USB, every peripheral needed its own proprietary connector. MCP does the same thing for AI integrations: one protocol, any tool.
How It Works
MCP follows a server/client pattern. The AI model acts as the client. Each external tool or service runs as an MCP server that exposes three types of capabilities:
Tools
Functions the AI can call. When an agent needs to send an email, it calls the email MCP server's send_email tool. The server handles the actual API call to Gmail, Outlook, or whatever service is behind it.
Resources
Data the AI can read. A CRM MCP server might expose customer records as resources. The agent reads them without needing to know how the CRM's API works.
Prompts
Reusable templates that guide the AI's behavior for specific tasks. A reporting MCP server might include prompts for generating quarterly summaries in a particular format.
Why MCP Matters
Standardization
One interface for all tools. Whether the agent is talking to a database, a calendar, or a payment processor, the protocol is the same.
Security
Scoped permissions per server, with authentication handled at the server level. An agent only gets access to the specific tools and data each MCP server exposes — nothing more.
Composability
Agents can use multiple MCP servers simultaneously. An email agent might use Gmail MCP + Contacts MCP + Calendar MCP, all at once, in a single workflow.
Community
A growing ecosystem of open-source MCP servers means you're not building everything from scratch. There are already servers for Google Workspace, Slack, GitHub, databases, and hundreds more.
MCP + API: Better Together
Not everything needs MCP. Direct API integrations are sometimes simpler — especially for tightly coupled, custom business logic where you want full control over the request/response cycle.
The best architectures use both. MCP for standardized tool access — giving agents a broad, composable toolkit. APIs for custom business logic — where you need precise control, performance optimization, or proprietary workflows.
I help clients figure out which approach fits where, and build systems that use the right tool for each job.
Interested in building AI agents with MCP integrations?