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MCP can give ChatGPT and Claude a shared way to discover and call your SaaS tools, but it does not create one universal sign-in across both products. A user may authorize your service once for each configured host; the available documentation does not establish that authorizing in one automatically grants access in the other. The practical lesson is to treat MCP as a shared integration surface and build authentication, permissions, hosting, and setup for each host deliberately.
What “one sign-in” can—and cannot—mean
MCP standardizes a protocol surface for tools and data. It does not act as your identity provider, require every server to use OAuth, or make the ChatGPT and Claude connector experiences identical. The MCP authorization specification says, “Authorization is OPTIONAL for MCP implementations.” For a protected HTTP server, it describes OAuth-based authorization, metadata discovery, and bearer access tokens; the protocol also distinguishes human-delegated access from client-credentials use cases. Read the MCP authorization specification.
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In a product, “single sign-in” is best understood narrowly: a user signs in to your SaaS through a particular host’s connector and grants it the access your service permits. ChatGPT and Claude have separate connector configuration and administration flows, so plan for the user to authorize each one independently unless you have a documented mechanism that proves otherwise. Shared protocol does not mean shared session.
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The distinction is not which assistant is universally better; it is where the connector is configured, who controls it, and how access is granted and used. Product interfaces and availability can change, so use the current host documentation when implementing.
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| Area | ChatGPT | Claude |
|---|---|---|
| Configuration and administration | Custom MCP apps can be created, tested, and published through developer mode on the web for Business and Enterprise/Edu customers. The documentation describes full MCP support, including write/modify actions, as a beta rollout for Business, Enterprise, and Edu. Current ChatGPT MCP app guidance. | Remote connector setup differs by plan: Team and Enterprise include owner configuration, while Pro and Max users have user-facing setup options. Current Claude custom connector guidance. |
| Sign-in and authorization | OAuth configuration should expose metadata that the host can discover. OpenAI recommends checking that refresh tokens are actually issued when the provider uses refresh support. OpenAI authentication guidance. | The remote connector setup documents sign-in choices including “Sign in now,” “Sign in when needed,” and “No sign in,” as well as OAuth client identity choices. Claude connector setup options. |
| Where access is enabled | Workspace publishing and testing controls are part of the app flow described for supported workspace plans. Availability and permissions may vary as the beta rollout changes. | A configured connector can be enabled or disabled for an individual conversation, in addition to any organization-level administration. |
| Server reachability | Remote MCP server use is documented separately from private or on-premises access. OpenAI describes Secure MCP Tunnel for supported products. OpenAI MCP server guidance. | Remote connectors are reached from Anthropic’s cloud infrastructure, not from the user’s local machine. A server available only on a private network or blocked from that infrastructure will not work through this route. |
| API versus user-facing connector | ChatGPT workspace apps and OpenAI API use are distinct integration surfaces; do not assume setup steps for one apply to the other. | The Claude Messages API MCP connector is distinct from the Claude user-facing connector. For API calls, the caller supplies an OAuth access token and is responsible for refreshing it; the API connector supports remote HTTP transports and does not directly accept local STDIO servers. Claude Messages API MCP connector documentation. |
Design OAuth for discovery and renewal
Make the protected server discoverable
For a protected HTTP MCP endpoint, implement the authorization behavior required by your design and make the relevant OAuth metadata available to the host. OpenAI’s authentication guidance describes protected-resource metadata and a 401 WWW-Authenticate challenge as discovery mechanisms. It also gives redirect URI registration guidance that depends on issuer-identification requirements. Because these details are implementation-sensitive, follow the current guide rather than hard-coding assumptions from a generic example: OpenAI authentication guidance.
OAuth is a choice for protected implementations, not a feature supplied by MCP itself. If your SaaS already uses an established identity provider, evaluate whether it can serve the required OAuth flow rather than building authentication from scratch. The right choice depends on your service’s identity model, consent requirements, and host compatibility.
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Check that authorization can be renewed
A successful first login is not enough if the access token expires and the host cannot obtain a replacement. OpenAI specifically advises checking discovery metadata for offline_access or an equivalent scope and verifying that refresh tokens are actually issued. Without the necessary refresh support, a user may need to authenticate again after the original access expires. Treat refresh behavior as something to verify in the provider configuration, not an assumption that follows from enabling OAuth. ChatGPT OAuth and refresh-token guidance.
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Host the endpoint where the host can reach it
A remote connector only works if the relevant host infrastructure can reach the MCP server. For Claude’s remote connector route, the request originates from Anthropic’s cloud infrastructure. An endpoint accessible only on a developer laptop, inside a private subnet, or behind a firewall that blocks the host is not reachable by that connector.
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OpenAI documents Secure MCP Tunnel for private or on-premises servers in supported products. This is a separate deployment consideration from a publicly reachable remote MCP endpoint; confirm that the specific OpenAI surface you are targeting supports the tunnel. Do not assume that a tunnel for one product or API flow changes the network requirements of another. OpenAI MCP server and Secure MCP Tunnel documentation.
Choose the tools and permissions before exposing them
An MCP server can expose tools that read information, change records, or perform other actions. Make the permission boundary a product decision, not an accidental side effect of connector setup.
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- Expose the narrowest useful tool set. Keep read-only operations distinct from write or modify actions where your architecture allows it.
- Match scopes to the task. Request only the access needed for the tools the user is authorizing, and make the consent understandable.
- Make consequential actions explicit. Design the tool behavior so users can understand what will change before allowing a write operation.
- Account for workspace and conversation controls. ChatGPT documentation describes workspace app testing and publishing controls; Claude lets users enable or disable configured connectors per conversation. Those are different control points, not substitutes for authorization enforced by your SaaS.
Host-level activation determines whether a connector is available in a workspace or conversation. Your service still needs to enforce the user’s identity, permissions, and access to specific data on every request.
A practical implementation sequence
- Define the integration target. Decide whether you are building for ChatGPT workspace apps, Claude’s user-facing remote connector, an API connector, or multiple surfaces. Keep API setup separate from end-user connector setup.
- Specify the user and permission model. Decide whether access represents an individual user, a workspace, or a service account, then map that choice to the scopes and operations your SaaS exposes.
- Build and protect the remote MCP endpoint. Implement the protocol transport and tool behavior you need. If access is protected, configure OAuth discovery and token validation according to the relevant host guidance and MCP specification.
- Verify deployment reachability. Confirm that the intended host infrastructure can contact the endpoint. For private infrastructure, verify that a documented tunnel or supported deployment path applies to the exact product.
- Configure each host independently. Register the required app or connector settings for each service, including OAuth metadata and redirect details applicable to that host. Follow the current host UI and documentation.
- Test the complete authorization lifecycle. Test a new user authorization, permitted and denied tool calls, token expiry, refresh behavior, and revocation. Check separately in each host because a working flow in one does not establish that the other is configured correctly.
- Roll out with administration in mind. Decide who can add or publish the connector, how users enable it, and how administrators can restrict or remove access if needed.
What the “front door” lesson amounts to
Adding MCP is not just putting a protocol endpoint behind a chat window. The chat host becomes another entry point into your product, with its own setup path, network origin, authorization discovery, token lifecycle, and controls over when tools are available. A clean implementation makes those boundaries visible: one shared tool surface where it helps, separate host onboarding where the products differ, and authorization enforced by your SaaS rather than inferred from the presence of a connector.
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