What MCP means
The Model Context Protocol is an open protocol for connecting AI applications to tools and context. An MCP server can expose capabilities to a compatible client. Whether a particular product offers such a server is a separate question that requires verification.
Review data and actions
Before connecting a workspace, identify what the server can read, what it can change, and which user or service identity authorizes it. Treat a tool that can send messages or modify content differently from a read-only search tool. Require approval for consequential actions when your policies call for it.
Manage the connection
Use an approved provider and authorization flow. Limit permissions, review what data may leave the organization, and establish how to revoke access. Instructions returned from external content should not automatically override a user’s intent or organizational policy.
Example: a read-only research assistant
Consider an AI assistant asked to find a project decision. Before connecting a workspace through MCP, check whether the server is authentic, what conversations it can read, where retrieved content is sent, and whether the user is authorized to see it. Start with a narrow test space and non-sensitive material. If the server can also send messages, treat that as a separate action capability and apply the organization’s approval rules.
Choose the next step
| Situation | What to check |
|---|---|
| Read access requested | Review the data scope, identity, and destination. |
| Write action is available | Decide whether explicit approval is required for each action. |
| A connection is no longer needed | Revoke its authorization and review retained data. |
A practical checklist
- Confirm that the provider actually supports MCP.
- List the data and actions exposed to the client.
- Review authorization, privacy, and approval requirements.
- Test with limited access and document how to revoke it.