If you've been following the AI agent space, you've probably seen the acronym MCP — Model Context Protocol. Announced by Anthropic in late 2024 and rapidly adopted across the industry, MCP is emerging as the standard interface for connecting AI agents to external tools and data sources. Understanding it is increasingly essential for anyone building or deploying AI systems.
The Problem MCP Solves
Before MCP, connecting an AI agent to a tool meant building a custom integration: writing the API wrapper, defining the tool schema, handling authentication, and updating the agent's prompt to describe the tool. Multiply this by ten tools across five agents and you have a maintenance nightmare.
MCP standardises this. It defines a common protocol by which an agent can discover what tools are available on a server, understand what each tool does, and call those tools with structured inputs — all without any custom integration code.
How MCP Works
An MCP server exposes a set of tools. Each tool has a name, a description, and a defined input schema. When an AI agent connects to an MCP server, it automatically discovers the available tools by querying the server's tool list. The agent can then reason about which tools to use for a given task and call them with the appropriate parameters.
- Tool Discovery: The agent queries the MCP server to get a list of available tools and their schemas.
- Tool Selection: The LLM reasons about which tool(s) to call based on the task.
- Tool Execution: The agent calls the tool with structured parameters. The MCP server executes the underlying function and returns the result.
- Result Integration: The agent incorporates the tool result into its reasoning and continues toward the goal.
Why MCP Matters for Businesses
For businesses building AI systems, MCP means you can expose your internal APIs and data sources to AI agents using a single, standardised interface. Once you've built an MCP server for your order management system, any agent — not just one you built — can use it. This creates a reusable AI infrastructure layer for your organisation.
MCP in Practice with SentientOne
SentientOne is built on MCP natively. Every agent can connect to multiple MCP servers, and the platform handles connection management, status monitoring, and tool discovery automatically. This means your agents can access your business data — orders, products, customers, documents — without any custom integration work beyond building the MCP server once.
“MCP is to AI agents what REST was to web APIs — a standard that makes everything work together.”
The Ecosystem Is Growing Fast
The MCP ecosystem is exploding. Pre-built MCP servers exist for GitHub, Slack, Google Drive, PostgreSQL, Stripe, and dozens of other services. This means that connecting your AI agent to tools your team already uses can be done in minutes, not weeks. For businesses exploring AI agents, MCP compatibility is now a key criterion when evaluating platforms.