Model Context Protocol (MCP): definition

The Model Context Protocol is an open standard that lets an AI model connect to external tools and data sources through one consistent interface, instead of a custom integration for each one.

Before MCP, every time you wanted a model to touch a new system (a CRM, a database, a calendar) someone wrote a one-off integration. MCP standardises that connection, so a tool exposed once can be used by any MCP-aware model or agent.

For us it means the connectors we build for one client are genuinely reusable for the next, which is exactly why we keep our internal tools as MCP servers.

Related terms

AI agent: An AI agent is a system that uses a language model to decide which steps to take and which tools to call to finish a goal, instead of just answering a single prompt.
Workflow automation: Workflow automation is the practice of moving a repeatable business process from manual steps to a system that runs it end to end, with a person involved only where judgement is actually needed.
Large language model (LLM): A large language model is a neural network trained on huge amounts of text to predict the next token, which lets it write, summarise, classify, and answer questions across almost any topic.