You may already be accustomed to asking AI assistants to summarize documents or draft emails. The next step that is increasingly being offered is to allow AI to open calendars, read shared folders, or fill in data in work applications. The connector behind this step is called the Model Context Protocol, abbreviated as MCP. Before connecting it to office tools, it is advisable for you to understand what is actually being opened up.
What is MCP, in one paragraph
The official MCP documentation refers to it as an open standard for connecting AI applications with external systems: data sources such as local files and databases, tools like search engines and calculators, and workflows in the form of specific commands. The analogy used in the documentation is a USB-C port: one type of plug that works for many devices. Without this standard, each AI application would need its own custom connector for every service.
This means that MCP itself is not intelligence and not a product. It is merely a pathway. Your AI's capabilities increase because the pathway is connected to something that has data and can perform actions.
What changes: from reading to acting
As long as AI only answers questions from the text you paste, the risks are limited to the content of that text. Once it is connected via MCP, it can access your data and take actions on your behalf. The MCP documentation itself mentions this benefit for end users, which is also why security is taken seriously.
The MCP security guidelines contain several categories of risks. Three that are most relevant for non-technical teams:
Local server running on your computer. The local MCP server operates with the same access rights as its client application. The official guidelines provide an example of an installation command that seems reasonable but secretly sends an SSH key file to an external address. Therefore, clients that support one-click installations must display the complete command and request explicit approval. If such a dialog appears, read the command carefully and do not approve it immediately.
Overly broad permissions. The same guidelines discuss "scope minimization": tokens that have very broad coverage, such as access to all files or entire databases, increase the damage if the token leaks and make audit trails hard to read. The principle is to start with minimal read permissions and then increase them only when specific actions are indeed necessary.
Intermediate servers that fail to check the token's destination. The MCP authorization specifications emphasize that servers should only accept tokens that are indeed issued for them and are prohibited from forwarding tokens from clients to other services. This is the responsibility of the server creators, but for organizations choosing third-party servers, this is a reason to inquire how that server handles tokens.
Questions worth asking before connecting
The GitHub guide for building remote MCP servers emphasizes that authentication alone is not enough. Every data access should ideally be limited according to the identity of the user currently working, and each server should only hold the secrets it needs. From the user's side, the practical takeaways are roughly as follows:
- Who created this server, and can you trace its documentation?
- Is it running on your computer or on a remote server, and what access rights does it request?
- Can permissions start from read-only?
- Do actions that modify data, such as deleting, sending, or writing, still require your approval?
- Is there a log that shows what AI has done on your behalf?
This list is not a guarantee of security. It merely makes the decision to connect feel like granting access to a new employee: you know who they are, what they hold, and what they cannot change without asking.
Start small
A sensible approach for teams just trying it out is to connect one low-risk data source first, with read permissions, and then assess the results before adding more access. One calendar or one sample folder is sufficient to see how AI uses the provided context and how often it requests additional permissions.
Note: the MCP security guidelines are continuously updated along with the specifications, so check the official documentation for the latest provisions before implementing it in your organization.