How to Make Your Product Available to ChatGPT, Claude and Cursor

Intro

A growing number of product and engineering teams are asking the same question: how do we get our product to show up inside ChatGPT, Claude, or Cursor, so a user can just ask their AI assistant to do something in our app instead of opening it separately?

AI chat and coding assistants are quickly becoming a place where people get work done, not just a place where they ask questions. If your product isn’t reachable from inside that conversation, you’re invisible in a channel your users are already spending time in.

The short answer: all three platforms — ChatGPT, Claude, and Cursor — connect to external products through the same open standard, called the Model Context Protocol (MCP). Build one MCP server for your product, and it becomes usable across all of them, plus every other MCP-compatible AI tool that comes after. You don’t need three separate integrations. You need one well-built server and a bit of platform-specific packaging.

Here’s what that actually involves.

1.

You Don’t Need to Know the Word “MCP” to Need This

Most teams get here without ever having searched for “MCP.” They start from a business question: our customers already live in ChatGPT or Claude for research and drafting, or in Cursor for coding — how do we get in front of them there? The technical answer to that question happens to be the same regardless of which platform triggered it, which is worth knowing before you evaluate three separate “integrations” that are actually the same piece of infrastructure wearing different labels.

MCP standardizes how an AI agent discovers what your product can do and calls it — reading data, taking actions, returning results — without you writing a custom, one-off connector for every AI platform your users happen to use. You build the server once; ChatGPT, Claude, Cursor, and anything else that speaks MCP can connect to it.

2.

How Each Platform Actually Connects to Your Product

The protocol is shared, but each platform wraps it differently, and that difference matters for planning.

ChatGPT: Apps (formerly Connectors)

OpenAI’s mechanism is now called apps — as of December 2025, what used to be “connectors” and “custom connectors” were unified under that name. There are two paths in:

• Developer Mode, where an individual or workspace admin points ChatGPT at a remote MCP server URL and starts using its tools right away — useful for internal or early-access use, not for reaching the general public.
• The Apps SDK, the path for publishing something any ChatGPT user can install from the app directory, with an OpenAI review step before it goes live.

One constraint worth knowing early: ChatGPT only connects to remote MCP servers reachable over HTTPS. A locally-running server has to be exposed as a hosted endpoint before ChatGPT can use it at all.

Claude: Connectors Directory and Custom Connectors

Anthropic’s equivalent is the Connectors system. Verified integrations live in a directory anyone can browse and enable in one click; anything else — including a server you build yourself — is added as a custom connector by pasting in its remote MCP server URL. On claude.ai, Claude Desktop, and Cowork, that connection runs through Anthropic’s cloud infrastructure rather than the user’s own device, so your server needs to be reachable from the public internet the same way it does for ChatGPT. Getting listed in the public directory instead of staying a manually-added custom connector goes through Anthropic’s own MCP server submission and review process.

Cursor: Built for the Coding Workflow

Cursor’s audience is developers working inside an IDE, so the mechanics look more like installing a plugin than enabling a consumer connector. Servers are added through a settings UI, a config file, or a one-click “Add to Cursor” link from your own documentation, and community servers are discoverable through a public directory. Cursor supports both local and remote servers, which makes sense given its developer audience — but if what you’re building is meant for a broad user base rather than engineers, the remote, hosted path is still the one that matters.

The Pattern Underneath All Three

Different names, different UI, same underlying requirement: a properly built, publicly reachable MCP server with well-designed tools, sane authentication, and — if you want real reach rather than a manually-pasted URL — a submission that passes each platform’s review process. That last part is where “we have an MCP endpoint” and “we’re actually discoverable inside ChatGPT, Claude, and Cursor” start to diverge.

3.

Why “We Built an Endpoint” Isn’t the Same as “We’re Available”

This is the part that catches teams off guard. Spinning up a bare-bones MCP server that technically responds to requests is genuinely fast. Making it something a platform will list, a security team will approve, and an agent will use correctly is a different amount of work — and it’s the same amount of work regardless of which platform you’re targeting first, because the gaps are structural, not platform-specific:

• Tool descriptions that agents can actually reason about. A tool named get_data with a one-line description forces the model to guess. Well-scoped, clearly described tools are what determine whether the agent calls the right thing at the right time — this is core design work, not a formality.
• Authentication that matches how these platforms actually connect. OAuth flows, session handling, and permission scoping all differ from a typical API key setup, and each platform has its own expectations around how that flow behaves.
• A hosted, remote deployment that stays up. All three platforms increasingly expect a reliable, publicly reachable endpoint — not a process running on someone’s laptop.
• A review-ready submission. Getting into Claude’s Connectors Directory or OpenAI’s app directory means meeting their security and quality expectations, not just having a working URL.
• Separation between safe and consequential actions. Read-only discovery tools versus tools that write or change data need different authorization levels — something that matters even more once an agent, not a human, is deciding when to call them.
• Monitoring once it’s live. Once your server is reachable from three major AI platforms, you need visibility into what agents are actually calling and why.

None of that shows up when you first stand up a minimal server locally and confirm it responds. It shows up the moment you try to actually launch — which is exactly the gap between a working prototype and a product genuinely available inside ChatGPT, Claude, and Cursor.

4.

Getting There: How Touchlane Builds This for ChatGPT, Claude, and Cursor

At Touchlane, we design, develop, and deploy custom MCP servers that make your product reachable from Claude, ChatGPT, Cursor, Perplexity, Microsoft Copilot, and the rest of the MCP ecosystem — through one implementation, not a separate integration per platform.

We cover the full path from a working endpoint to something actually listed inside the tools your users open:

• A production server, hosted and publicly reachable — the remote, HTTPS-accessible endpoint all three platforms expect, not a process running on someone’s laptop
• Per-platform packaging — the connector/app setup and OAuth flow each of ChatGPT, Claude, and Cursor expects on top of the shared protocol
• Review-ready submissions — meeting the security and quality bar for Claude’s Connectors Directory and OpenAI’s app directory, so you’re discoverable rather than manually pasted in
• Tools designed for agent reasoning — clearly scoped, clearly described tools that get called correctly, not a get_data black box
• Permissioning that separates safe from consequential — read-only discovery kept apart from write actions, with the guardrails a security review will ask about
• Monitoring across platforms — visibility into what agents call once your server is reachable from several AI tools at once
• Ongoing maintenance — keeping the server current as each platform’s requirements and the MCP spec evolve

What tends to set our approach apart: deep backend and API expertise going in — this is a backend architecture problem before it’s an AI problem — hands-on experience building AI-powered systems, fast delivery cycles, and end-to-end ownership from architecture through launch, including the security and compliance work each platform’s review process expects.

Whether you’re planning your first MCP server or looking to expand an existing AI integration strategy, we’ll help you define the right architecture, scope, and implementation approach. 

Conclusion

ChatGPT, Claude, and Cursor each have their own name for it — apps, connectors, MCP servers in a settings panel — but underneath, they all speak the same protocol. That’s the practical takeaway: you’re not scoping three integrations, you’re scoping one piece of infrastructure and three packaging steps on top of it.

The part that actually determines whether you show up, though, isn’t the protocol — it’s the execution. A bare endpoint that technically responds to MCP requests is not the same thing as a server with well-scoped tools, proper authentication, production-safe permissioning, and a submission that clears each platform’s review bar. That gap is where most DIY attempts stall, and it’s the difference between “technically possible” and “actually available to your users.”

If your product has an API, workflows, or data worth putting in front of an agent, the path there is well understood at this point — it just needs to be built properly the first time.

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