ChatGPT vs Claude Integrations: Apps, MCP, and Shared Context
ChatGPT and Claude each connect to external work tools, but they use different mechanisms and permission models. Here is a clear comparison of how each connects, what it can read and update, and where the practical differences show up.
Updated 7 min read
ChatGPT and Claude both connect to external apps, but they do it through different mechanisms that carry different permission models and different limits. Knowing what each can actually reach—and where it stops—prevents the assumption that a connection you set up in one tool automatically works the same way in the other.
For teams using both, the question that matters most is whether your shared work record travels with you. Tools like fenbs sit outside both assistants and connect to each over MCP (a standard way for an AI app to use an outside tool), so the same project context, rules and progress are available regardless of which assistant your team member opens.
| ChatGPT | Claude | |
|---|---|---|
| Built-in connectors | Google Drive, OneDrive, Slack, Jira and others via ChatGPT Work | Google Drive, Gmail, GitHub, Jira, Confluence and others via Settings → Integrations |
| Open protocol support | Developer-mode app connections; no native open MCP support in the standard UI | MCP (Model Context Protocol) supported natively in Claude Code and via developer connections |
| Project-level context | ChatGPT Projects: organize conversations; per-project instructions | Claude Projects: per-project instructions, uploaded files and connector sources |
| Custom extensions (retiring) | Custom GPTs with Actions (retiring December 11, 2026) | No direct equivalent; MCP servers fill this role for developers |
| Permission model | Access granted per connector; ChatGPT Work manages scope | Access granted per connector or MCP server; each server is approved individually |
| Shared across assistants | No—native memory stays in ChatGPT | No—native memory stays in Claude |
| fenbs compatibility | Connects via MCP in developer mode | Connects via MCP natively in Claude Code and Claude.ai |
How ChatGPT connects to work tools
ChatGPT's integrations are primarily built-in connectors managed through the ChatGPT Work interface. These let ChatGPT read from and in some cases write to services like Google Drive, Slack, and Jira without you building anything. You authorize the connection once and ChatGPT can reference content from those services in your conversations.
Custom GPTs—configured assistants that could include Actions pointing at third-party services—are retiring on December 11, 2026. Before relying on a Custom GPT integration in your workflow, check OpenAI's documentation to understand what replaces it.
ChatGPT also accepts developer-mode connections through apps you register in the OpenAI platform, which allows tools like fenbs to connect via MCP. Which plans support developer mode is OpenAI's to confirm; check their help pages rather than assuming availability.
How Claude connects to work tools
Claude has built-in connectors similar to ChatGPT's—for Google Drive, Gmail, GitHub, Jira, Confluence and others—set up in Settings → Integrations on Claude.ai. These apply across your account and are available in any conversation.
Claude also supports MCP natively. In Claude Code, you can register MCP servers at user or project scope. MCP (Model Context Protocol) is an open standard that lets an AI app call an outside tool in a structured way—similar in purpose to how an app uses an API, but designed specifically for AI assistants. Any tool that publishes an MCP server can connect to Claude without waiting for a built-in integration.
This is the key difference for developers: Claude Code's native MCP support means tools like fenbs connect without requiring developer-mode approval or custom configuration on OpenAI's side. For a deeper look at how Claude connects, see Claude vs ChatGPT.
What each integration can read and update
Both ChatGPT and Claude grant access per connector. When you connect Google Drive, you authorize Claude or ChatGPT to read files from Drive—not from your email, your calendar, or an unrelated service. Each connection operates within the permissions you granted when you authorized it.
Read access is more commonly available than write access. Some connectors let the assistant draft or create content on your behalf; others are read-only. Check the connector's documentation before assuming it can update a record, send a message, or create a file.
MCP servers can be configured with whatever read and write permissions the server implements. An MCP server for a task board might allow reading tasks, adding comments, and moving tasks between lanes—all actions you would want an assistant to take during a work session.
One work record both assistants can read
fenbs connects to Claude natively over MCP and to ChatGPT via MCP in developer mode. Both see the same project context: Rules first, then Where we left off, In progress tasks and any context notes a previous session left. The work record lives in fenbs, not in either AI app, so switching between assistants does not lose it. Set it up once from Integrations.
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Where shared context breaks down
Both ChatGPT and Claude have their own memory features, but those memories are local to each app. A rule you teach ChatGPT through its memory system does not appear when you switch to Claude. A decision you record in Claude's Project instructions is not visible when a colleague opens the same project in ChatGPT.
This is the problem a shared external record solves. If the rule is written in fenbs and both assistants connect to fenbs via MCP, both see the same rule. The colleague's ChatGPT and your Claude Code are both reading from the same source. See ChatGPT projects vs Claude projects for a detailed look at how those per-app project features compare.
The practical implication: use built-in connectors for reading source material from services like Drive, Jira or GitHub. Use a shared external work record for the decisions, rules and progress that need to be consistent across assistants, teammates and sessions.
Permissions and what a connected assistant can do
In fenbs, every connected assistant acts as the person who approved its connection, within that person's role and the access they granted. An assistant cannot take an action that person could not take. That constraint applies to both ChatGPT and Claude connections—the rule is in the work record tool, not in the AI app.
Both assistants can propose actions and record information, but only a person signs off on decisions, pre-approves work, or makes choices about direction. The assistant's connection is authorized, not trusted unconditionally. That distinction is worth making explicit in any tool that holds important project context.
A team member using ChatGPT and a developer using Claude Code can share one fenbs board. Roles control what each person and their assistant can read and update. Decisions appear in both sessions, and History records who changed what. That shared record is the practical answer to the "which assistant does my team use?" question—the work record outlasts any single tool choice.
How to do this with fenbs
- Create a free account. You get a personal kanban board to start; add a team workspace when colleagues need access.
- Add your project, the current tasks, and the standing rules in Decisions and rules. Keep the first version focused on what the next session needs to know.
- Connect Claude Code from Integrations. Follow the MCP setup guide for your version of Claude.
- For ChatGPT, follow the developer-mode MCP connection guide on the Integrations page. Both assistants then read from the same fenbs board.
- At the start of each session, ask your assistant to read the project context. Before stopping, ask it to save progress on the tasks it touched.