Agents & automation

What is the Model Context Protocol?

MCP reduces the custom plumbing needed to give AI access to your systems, which lowers integration cost and avoids lock-in to one vendor's connectors.

In plain terms

The Model Context Protocol is an open standard for connecting AI models to external tools and data sources through a consistent interface.

Also called MCP.

The Model Context Protocol, or MCP, is a shared way for an AI app to connect to tools and data. Instead of a custom plug for every pair of products, a tool speaks MCP and several assistants can use it. Think of it as a standard socket, not a new kind of intelligence.

A standard does not make a connection safe. An MCP tool that can read a drive can read whatever the connection allows. Treat each connection like a vendor integration. Name the data it can see, the actions it can take, and who approved it. Shadow connections, the ones a team turns on over a weekend, are the risk.

Think of it this way: Before MCP, connecting an AI to every new tool required custom wiring each time. MCP is the universal power socket: one standard interface and any compatible plug fits.

A company standardizes all internal AI integrations on MCP. Their engineering team adds a new data source in hours instead of weeks, because the interface contract is already defined and reused.

An analyst connects a chat assistant to the company's file store through an MCP tool so it can answer from the quarterly folder. The scope was supposed to be that folder. The connection had access to the whole drive, including HR files. The fix was the permission, not a smarter model.

When building AI systems that need to connect to multiple internal or third-party tools, and you want to avoid rebuilding integrations for every new model or vendor. For simple, single-tool integrations where the overhead of a protocol is not worth the standardization benefit. MCP pays off at scale and across multiple integrations.

RaftLabs builds agents that do one defined job, with a person still on the steps that cannot be undone. You see the workflow on your own data before it runs on its own. The related work on our side is MCP server development.

This sits with the other agents & automation terms on the glossary. When software stops only answering and starts carrying out the work. Worth reading next: AI Agent, Agentic AI, and Multi-agent System.

Common questions

No. It helps when you want one assistant to reach several tools without a custom plug each time. A single, well-built connection to one system does not require it. Adopt it when you have more than one assistant or more than a couple of tools to connect.
The data it can read, the actions it can take, the account it uses, and how you turn it off. A read of approved policies is a different decision from a write into customer records. If a team cannot name those four things, the tool is not approved.

Work with us

Tell us what's broken.

Tell us what's not working in your business. We'll find the real problem and tell you exactly what it would take to fix it.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.