The Model Context Protocol, or MCP, is an open standard that allows large language models to connect directly with applications, databases, and services your company already uses. Instead of being limited to answering with what they "know" from their training, models can access in real time your CRM, task manager, code repository, or any system that exposes an MCP server. The result is an AI that works with your actual information, not with generic answers.
Anthropic, along with other companies in the AI ecosystem, has driven this protocol to solve a concrete problem: artificial intelligences are powerful, but isolated. Without a standard way to connect them to the work environment, each integration requires custom development. MCP unifies those connections and allows tools from different brands to speak the same language with the models.
In day-to-day work, MCP turns AI into a copilot that can read your emails, check your calendar, search your documentation, or execute actions in your tools. An agent can, for example, review open tickets in your support system, cross-reference them with your internal knowledge base, and propose coherent responses. Or it can analyze recent commits in your repository and suggest code improvements following your team's conventions. The possibilities depend on which MCP servers you have deployed and what permissions you grant them.
The important thing is that the connection is bidirectional: the AI doesn't just read data, it can invoke actions. Creating tasks, sending messages, updating records, or triggering workflows are operations a model can coordinate if the MCP server allows it. That brings AI closer to a real executive assistant, one that doesn't just advise but acts within the limits you define.
The first step is to identify where your team loses time on repetitive tasks that involve switching between multiple tools. If every time a customer asks something you have to search across three different systems, or if generating reports means copy-pasting data from several spreadsheets, there are clear candidates. MCP is especially useful when the relevant information is scattered and a human has to manually orchestrate the query.
Another signal is the need for up-to-date answers. If your team needs the AI to know the current state of projects, inventory, or reservations, a model without MCP can only work with what it has in memory. With MCP, each query can go to the source of truth and return current information. Typical areas where this impacts are customer service, operations, sales, and product development.
Finally, start small. You don't need to connect the entire company at once. Choose a well-defined process —for example, responding to frequent questions based on your documentation— and validate that AI with MCP improves the result. Once the value is proven, you can expand to more tools and more use cases. The protocol is designed to grow modularly and securely.
