Guide
Do MCP connectors work with other AI tools?
The Model Context Protocol is not a Claude-only technology. It is an open standard multiple AI clients implement, which changes what a custom connector actually is: an asset, not a lock-in.
Yes. MCP is an open standard, governed under the Linux Foundation since December 2025, and it is supported by multiple AI clients including ChatGPT, Cursor, and VS Code alongside Claude. A well-built MCP connector is therefore portable. Client capabilities differ, especially around auth and feature support, so builds still target a primary client first.
Key takeaways
- MCP is an open standard governed under the Linux Foundation, not a proprietary Anthropic interface, so no single vendor controls it.
- Multiple clients speak it today: Claude, ChatGPT connectors, Cursor, VS Code, and a growing tail of IDEs and agent frameworks.
- Portability is real but not free: auth flows, feature support, and confirmation behavior vary by client, so the same server can need per-client adjustments.
- That is the strategic point of a custom connector: the mapping of your software into safe, structured tools carries forward to whichever assistant your team uses next.
Is MCP an Anthropic product or an open standard?
An open standard. Anthropic created the Model Context Protocol and open-sourced it in November 2024, and since December 2025 the specification has been governed under the Linux Foundation, with multiple vendors participating. No single company controls it, which is what makes a connector built against the spec portable across clients.
This distinction matters for anyone deciding whether to invest in a connector. A proprietary integration format lives and dies with its vendor. An open, foundation-governed specification is a different kind of dependency: the spec, the SDKs, and the ecosystem around it are public, and the incentive to keep clients compatible is shared across the industry.
Practically, it means an MCP server is written once against a published protocol at modelcontextprotocol.io, not against Claude's internals. Claude is one consumer of it. Any client that implements the spec is another.
Which AI tools support MCP besides Claude?
Support is broad and growing. ChatGPT connects to remote MCP servers through its connector system, Cursor and VS Code speak MCP for coding agents, and a long tail of desktop clients, IDEs, and agent frameworks implement the spec. The same protocol your Claude connector speaks is what these clients consume.
The client list stopped being a Claude story some time ago. The pattern that matters is the categories:
- General assistants: Claude and ChatGPT both consume remote MCP servers as user-facing connectors.
- Coding tools: Cursor, VS Code, and other MCP-capable editors use servers to give their agents tools and context.
- Agent frameworks: developer platforms for building autonomous agents commonly speak MCP as their tool layer.
For a business, the second-order effect is the interesting one: the connector you commission for conversational use in Claude is the same class of asset a future internal agent, coding workflow, or other assistant can consume.
Does the same connector work identically in every client?
No, and it is worth being honest about that. Clients differ in the auth flows they support, which protocol features they implement, how they surface confirmations, and what their plans allow. A connector that runs perfectly in Claude may need adjustments elsewhere. Portable means the core carries over, not that setup is identical.
The protocol defines the contract, but each client chooses how much of it to implement and how to expose it to users. In practice the differences cluster in a few places:
- Auth: which OAuth flows a client supports for remote servers, and how it handles tokens, varies meaningfully between clients.
- Feature support: newer protocol capabilities land in different clients at different times, so a server may need graceful fallbacks.
- Confirmation behavior: how a client presents a proposed write action for human approval differs, which matters for gated tools.
- Plan gating: which subscription tiers can add custom remote servers is each vendor's own policy, not the protocol's.
None of this undermines portability; it defines its shape. The tool design, schema mapping, and gating logic, which is most of the work, carry over. The client-facing edges sometimes need tailoring.
Why build for Claude first?
Because a connector should be excellent somewhere before it is adequate everywhere. Our builds target Claude's remote connector support first: its auth flow, its tool-use behavior, and its human-in-the-loop confirmation pattern for gated writes. Since the result is a standard MCP server, that Claude-first build remains usable from other MCP clients.
Targeting one client first is an engineering decision, not a loyalty statement. Tool descriptions get tuned against how that client's model actually calls them, auth is verified end to end in that client's flow, and gated writes are tested against how that client surfaces confirmations. That focus is what makes a connector feel native instead of merely functional.
Claude is the natural primary target for the conversational business use these connectors are built for, and it is where our clients work. But because the deliverable is a spec-compliant MCP server rather than a Claude plugin, choosing Claude first does not spend your portability. It sequences it.
Is a custom connector a safe investment if assistants change?
That is the strongest reason to build one. The connector encodes your tool's schema, your chosen actions, and your gating rules in an open protocol that no single vendor controls. Assistants will keep changing. The work of mapping your software into safe, structured tools carries forward to whichever client wins your desk.
Strip the branding off a custom connector and what remains is durable: a careful map of your software's objects and actions, a deliberate list of what an AI may read and write, and confirmation gates on the steps you consider risky. None of that is specific to any assistant. It is specific to your business.
Model vendors will leapfrog each other, and the client your team prefers in two years may not be the one it prefers today. An integration written against an open standard is the hedge: switch clients and you re-point the connector, not rebuild the thinking.
The asset framing
Treat a connector the way you treat your data model, not the way you treat a vendor plugin. It is infrastructure that sits between your systems and whichever AI clients you adopt, and it appreciates as more clients speak the protocol.