ChatGPT Enterprise Integrations and MCP Setup

ChatGPT Enterprise and team workspaces give administrators a dashboard for controlling which tools and integrations members can use. The admin's job is to evaluate each integration for data handling and permissions, enable the ones that meet your organization's requirements, and document the approved setup so everyone knows what is available and why.

MCP (the Model Context Protocol, a standard way for an AI app to connect to an outside tool) adds a way for ChatGPT to reach a shared project record. Tools like fenbs hold that record: the team's projects, tasks, decisions, and rules in a place every connected assistant reads before starting work. For broader workspace guidance, see ChatGPT enterprise for teams.

What the admin dashboard controls

The admin dashboard in Enterprise and team workspaces is where you turn capabilities on or off for your organization. Built-in capabilities — web search, code execution, file handling, image generation — can be enabled or disabled per workspace. Connectors, which let ChatGPT read and update outside services, have their own section and usually need explicit approval before members can use them.

Admins also control which users have access to advanced features. Some capabilities, including developer mode for MCP, may be restricted to specific users or groups rather than available to the whole workspace. Check your current dashboard for the exact options available in your plan.

Connectors: connecting outside data to ChatGPT

Connectors let ChatGPT read from services like Google Drive, SharePoint, or Slack — within the scope of the access each connector is granted. In Enterprise, admins typically review and approve connectors before they appear for members. The review should cover what data the connector can access, how long that data is retained by OpenAI, and whether it meets your data handling requirements.

Each connector asks for specific OAuth scopes when a user authorizes it. A connector that needs read-only access to a specific folder is narrower than one that requests access to an entire drive. Where possible, authorize the narrowest scope that covers the actual use case.

• Review the connector's documentation for the scopes it requests.
• Check OpenAI's documentation for data handling and retention for connected services.
• Enable the connector in the admin dashboard before members can add it.
• Document which connectors are approved and for what purpose.
• Review connector usage periodically and remove access that is no longer needed.

MCP in Enterprise and team workspaces

MCP connections in ChatGPT allow the assistant to reach external tools and services through a standard protocol. Setting up an MCP connection requires developer mode in the ChatGPT client. Which plans include developer mode is OpenAI's to say — check their help center for the current list before planning a rollout.

When developer mode is available, a user can add an MCP server in their ChatGPT settings. The server appears as a set of tools the assistant can call during a conversation. As an admin, you can decide whether developer mode is available to all members or a subset, and you can document which MCP servers have been reviewed and approved for use.

For integration testing and managing multi-tool setups, see integration testing.

Evaluating an integration before you enable it

For each integration you consider, work through the same checklist. Start with data: what does this integration send to OpenAI, and under what terms? Then check permissions: what access does it need to external services, and is that scope appropriate for your members' typical use? Finally, check maintenance: does the integration have a support contact, recent updates, and a clear security disclosure process?

For integrations that access sensitive business data, involve your security or legal team in the review. A connector that reads documents from your corporate drive is different in risk profile from one that reads public web pages. Document the review outcome so future admins understand which integrations were approved and what conditions they came with.

Set a review cadence for active integrations. Access that made sense when a project started may be too broad after the project ends. Revoke connectors that are no longer in regular use, and check whether individual users have authorized connections the organization has not reviewed. An integration left active and unreviewed is a quiet risk.

For how Claude compares for team settings, see Claude vs ChatGPT. For a broader look at alternatives, see ChatGPT alternatives.

How to do this with fenbs

1. Create a free account and set up a team workspace in fenbs for your project.
2. Add team members and assign roles that reflect what each person's connected assistant is allowed to do.
3. Connect ChatGPT Work to fenbs over MCP from /integrations. In ChatGPT, this requires developer mode — check OpenAI's help center for which plans include it.
4. Add the organization's approved rules and the current project context to Decisions and rules and the project notes in fenbs.
5. Ask the assistant to read the Rules and Where we left off at the start of each session, and to save progress before it stops.

For a comparison of how ChatGPT and Claude handle team integrations, see Claude vs ChatGPT. For an overview of Gemini in a similar context, see Gemini vs ChatGPT.