What Is MCP?
Model Context Protocol (MCP) is an open standard — originally created by Anthropic — that defines how AI agents connect to external tools, databases, and APIs. Think of it as a universal adapter between AI models and the systems they need to interact with.
Before MCP, every AI integration was custom. If you wanted your agent to access your CRM, you wrote custom code. If you wanted it to search your database, more custom code. Every tool, every integration, every connection was bespoke.
MCP changes that. It provides a standardized protocol — like HTTP for the web or USB for hardware — that lets AI agents discover and use tools through a consistent interface.
Why MCP Matters
The numbers tell the story: 75% of API gateway vendors are expected to integrate MCP support by 2026. It's on every CTO's agenda because it solves three critical problems:
1. Interoperability
With MCP, an AI agent built with Claude can use the same tools as one built with GPT-4 or Mistral. The tool integration is decoupled from the model.
2. Composability
MCP servers are modular. You can mix and match — a CRM MCP server, a calendar MCP server, a database MCP server — and your agent uses whichever it needs for the task at hand.
3. Security
MCP includes built-in authentication and authorization. Instead of giving your AI agent raw API keys, MCP servers mediate access with proper scoping and audit trails.
How MCP Works
The architecture is straightforward:
AI Agent (Client) <--MCP Protocol--> MCP Server <--API--> External System
MCP Client: The AI agent that needs to use tools. It discovers available tools, understands their capabilities, and sends requests.
MCP Server: A lightweight server that exposes tools, resources, and prompts. It handles the connection between the AI agent and the external system.
Transport Layer: MCP supports multiple transports — stdio for local tools, HTTP/SSE for remote servers.
What MCP Exposes
| Concept | What It Is | Example |
|---------|-----------|---------|
| Tools | Functions the agent can call | schedule_appointment(date, time, patient_id) |
| Resources | Data the agent can read | Customer records, knowledge base articles |
| Prompts | Pre-built prompt templates | "Summarize this patient's history" |
MCP vs. Other Approaches
MCP vs. Function Calling
Function calling (as in OpenAI's API) is model-specific. MCP is model-agnostic. Function calling defines what a tool does inline with the prompt. MCP servers are standalone services that any agent can discover and use.
MCP vs. Google A2A (Agent-to-Agent)
A2A is about agents communicating with each other. MCP is about agents communicating with tools and data. They're complementary, not competitive. In a multi-agent system, agents might use A2A to coordinate while each uses MCP to access tools.
MCP vs. Custom API Integrations
Custom integrations give you maximum control but minimum reusability. MCP servers are reusable across agents, models, and projects. Once you build an MCP server for your CRM, any AI agent in your organization can use it.
Building MCP Servers
An MCP server is surprisingly simple to build. Here's the core concept:
// Define a tool
server.tool("get_customer", {
description: "Look up a customer by email",
parameters: {
email: { type: "string", description: "Customer email" }
},
handler: async ({ email }) => {
const customer = await db.customers.findByEmail(email);
return { content: JSON.stringify(customer) };
}
});
The server advertises its tools. The AI agent discovers them, understands when to use them (from the descriptions), and calls them as needed during a conversation.
Real-World MCP Use Cases
Customer Support Agent
MCP servers for: CRM (customer lookup), ticketing system (create/update tickets), knowledge base (search documentation), communication (send emails/SMS).
Healthcare AI Agent
MCP servers for: EHR system (patient records), scheduling (appointment management), insurance (verification workflows), communication (patient notifications).
Sales Automation
MCP servers for: CRM (lead management), calendar (meeting scheduling), email (outreach), analytics (pipeline reporting).
Security Considerations
MCP is powerful, which means security matters:
1. Server-side authentication — MCP servers should authenticate with external systems using service accounts with minimal required permissions.
2. Client authentication — Not every agent should access every MCP server. Implement proper client authentication and authorization.
3. Input validation — MCP servers must validate all inputs from agents. LLMs can generate unexpected inputs — your server needs to handle them safely.
4. Audit logging — Log every tool call, including the agent that made it, the parameters, and the result. This is essential for debugging and compliance.
5. Rate limiting — Prevent runaway agents from overwhelming your systems.
Getting Started with MCP
If you're building AI agents, MCP should be part of your architecture from day one. Here's our recommended approach:
- Identify your core integrations — What systems does your agent need to access?
- Build MCP servers — One per system, with well-defined tools and resources.
- Test independently — Verify each MCP server works correctly before connecting agents.
- Connect and iterate — Wire up your agent, test end-to-end, and refine tool descriptions for better agent behavior.
At Autor, we build custom MCP servers and AI agents as part of our integration work. Whether you need to connect AI to your CRM, EHR, scheduling system, or internal tools — we can help.