Model Context Protocol (MCP)

Webisoft AI team Updated 2 min read

Model Context Protocol (MCP) is an open standard for connecting AI assistants to external tools and data sources, databases, file systems, CRMs, ticketing systems, and other business software, through one common interface instead of a custom integration per AI app per tool. Introduced by Anthropic in late 2024 and since adopted across the industry (including by OpenAI and Google), it’s often described as “USB-C for AI”: build one MCP server for a system, and any MCP-compatible assistant can plug into it.

The mechanics, simply: an MCP server is a small connector that exposes what a system offers, searchable data, callable actions like “create ticket” or “query the sales database.” An AI application (the client) connects to whichever servers you configure, discovers what they offer, and the model can then use those capabilities during a conversation, invoking them through function calling. MCP standardizes the plumbing between the two, so the ecosystem of ready-made connectors compounds instead of fragmenting.

MCP matters most for AI agents: an agent is only as useful as what it can reach, and MCP is becoming the standard way to give it reach.

Why it matters at work

Before MCP, wiring an AI assistant into company systems meant bespoke integrations that locked you to one vendor’s ecosystem. With MCP, the integration investment is portable: the connector your team builds for its order database works with today’s assistant and tomorrow’s, and a growing catalog of off-the-shelf servers (for common SaaS tools, databases, and file stores) shrinks the build list. The governance flip side arrives with the power: every connected server extends what the AI can see and do, so access control and audit on MCP connections deserve the same rigor as any system integration.

A work example

An IT team stands up one MCP server over its internal knowledge base; the same connector then serves the engineering team’s coding assistant, the support team’s chat assistant, and an internal agent, three integrations for the price of one.

  • Function calling, the model-side mechanism MCP tools are invoked through
  • AI agent, the systems MCP most powerfully extends

Get one practical AI-at-work idea in your inbox each week, subscribe to the AI Work+ newsletter.

FAQ

What problem does the Model Context Protocol solve? Before MCP, every AI application needed a custom integration for each data source or tool. MCP defines one open standard, so a tool exposed through an MCP server works with any MCP-compatible AI application.

Is MCP tied to a single vendor? It was introduced by Anthropic but released as an open standard. Multiple AI applications and tool makers have adopted it, and anyone can implement an MCP server or client.