MCP Server Hosting for AI Assistants: Claude, ChatGPT, Cursor

To use an MCP server with several AI assistants, host it remotely over HTTPS with streamable HTTP and give each client its own key or OAuth sign-in. ChatGPT requires a remote server because it cannot launch local processes. Editors such as Cursor and VS Code work with either local or remote servers.

What each kind of client wants

Clients fall into two groups with different needs.

Client type	Examples	Local stdio	Remote	Typical auth
Chat assistants	Claude.ai, ChatGPT, Gemini	Not for ChatGPT	Yes	OAuth through a connector screen
Desktop apps	Claude Desktop	Yes	Yes	Local none, remote OAuth or key
Editors	Cursor, VS Code	Yes	Yes	Bearer key or OAuth
Custom agents	Scripts, frameworks	Yes	Yes	Bearer key

The ChatGPT row is the one that forces the issue. BusinessMCP’s remote versus local guide says remote is the only option for ChatGPT. If your users are on ChatGPT, hosting is not optional.

One endpoint, several assistants

The efficient design is a single remote endpoint that every client uses. BusinessMCP’s MCP hosting page describes exactly this: one endpoint per workspace that Claude, ChatGPT, Gemini, Cursor or a custom agent can reach, with per-client setup instructions for Claude Desktop, ChatGPT, Gemini, Cursor and VS Code on its connect page.

Benefits of one endpoint:

• One place to manage tools, keys and logs.
• Tool descriptions stay consistent across assistants.
• Revoking an agent is a single action.

Drawbacks: a bug or outage affects every assistant, and one assistant’s quirks can constrain your tool design. Test tools in each client; models differ in how they choose and call tools.

Connection flow in practice

For a consumer assistant:

1. Add a custom connector and paste the server URL.
2. The assistant discovers authentication through protected-resource metadata.
3. The user signs in and approves a consent screen.
4. Tools appear and the model can call them.

For an editor or script:

1. Add the URL to the client’s MCP configuration.
2. Add a bearer key in a header.
3. Reload and confirm the tool list.

If the OAuth steps fail, check the metadata endpoints and redirect settings first. If the editor works but the assistant does not, the server may support only static keys. See hosting with authentication for the difference.

Key takeaway: design for the weakest client. If ChatGPT or a chat connector is in scope, you need remote hosting and OAuth from day one.

Designing tools that work across assistants

Assistants choose tools from names and descriptions, so write for the model:

• Use clear verbs and nouns in names.
• State in one or two sentences what the tool does and when to use it.
• Keep input schemas tight, with enums and bounds.
• Prefer few tools over many; BusinessMCP’s build guide recommends well-described tools over a long vague list.
• Return structured output, and mark failures with isError.

Governance across assistants

Different assistants mean different users and different risk. Give each assistant or agent its own key, scope tools per key, keep write tools off by default and log every call. These are the controls described in MCP server security. A managed endpoint can supply them, with a limit worth repeating: BusinessMCP says it does not host your own server code, so if you need a custom server, host that yourself and connect it as an upstream.

Bringing your own model key

Some platforms let you use your own provider key so model calls bill to your account. BusinessMCP says you can bring your own Anthropic, OpenAI or Google key. That matters for data handling and cost control; confirm the details on the vendor’s privacy page.

Rollout plan

1. Pick the first assistant and a read-only toolset.
2. Host remotely, with auth and logging.
3. Connect the assistant and test five real tasks.
4. Add the second assistant with its own key.
5. Compare answers and fix tool descriptions that confuse either model.
6. Add monitoring, as in MCP server monitoring.
7. Only then add write tools, behind approvals.

For the platform choice itself, our platform comparison lays out the options.

Choosing between managed and self-hosted for assistants

The choice depends on what the assistants need to reach. If they need your business tools and data, a managed endpoint gets you connected quickly. BusinessMCP’s MCP hosting exposes web analytics, CRM, Stripe revenue, ad spend, Search Console and AI-visibility results as tools, according to its page, and aggregates third-party servers you connect. If they need a bespoke internal system, you will probably write your own server, which means self-hosting it; the same page says a custom MCP server for an internal system may need a developer.

Need	Route
Assistants query analytics, CRM, revenue	Managed endpoint
Assistants call an internal system	Your own server, self-hosted
Both	Self-host the custom server, aggregate it behind a managed endpoint
Many SaaS apps, little governance	Integration platform, compare vendors

The decision framework in self-hosted versus managed goes deeper on the trade-offs.

Pitfalls specific to multi-assistant setups

Assuming identical behaviour. One model may call a tool with perfect arguments while another guesses. Test every tool in every assistant you support.

Shared keys. If three assistants share a key, you cannot tell them apart in logs or revoke one. Issue one each.

Overloading one tool. A tool that does many things confuses models. Split by intent.

Forgetting updates. Assistants change their connector flows over time. Keep a note of which clients you tested and when.

Ignoring cost. Each assistant that runs agents consumes runs or calls. Watch the meter, as the cost article explains.

Testing a hosted server across assistants

A repeatable test prevents the “works on my machine” problem when several assistants are involved. Prepare five prompts that cover your main tools: one simple lookup, one that needs two tools in sequence, one with an ambiguous request, one that should be refused because the key lacks scope, and one that triggers an error. Run all five in each assistant and record what happened.

Prompt type	What you learn
Simple lookup	Basic connection and tool selection work
Two-step task	Whether the model chains tools sensibly
Ambiguous request	Whether descriptions guide the model or mislead it
Out-of-scope request	Whether scoping holds when the model tries
Forced error	Whether error messages help the model recover

Review the server logs afterwards. They show the exact arguments each assistant sent, which is the fastest way to find a tool description that one model reads differently from another. Fix descriptions first, schemas second, and only then consider splitting or merging tools. Repeat the test whenever you add a tool or a client updates its connector flow, and keep the results with your runbook so the next person does not start from zero.