How to use MCP across all your AI tools

You have got Claude Code in the terminal, Cursor in the editor, and ChatGPT in the browser. Each one is sharp on its own and clueless about your actual project. MCP is how you fix that across all of them at once, instead of pasting context into each tool forever. Here is how to actually do it.

Step 1: Get the model right about what MCP connects

Quick reset so the steps make sense. MCP, the Model Context Protocol, lets an AI tool read outside context and call tools through one standard. A client lives in your AI tool; a server sits in front of your context. Connect a tool to a server once, and it reads what fits the task at the start of a session. Full primer in what is MCP.

The important part for "across all your tools": one server can serve many tools. You do not wire each pair by hand.

Step 2: Know which surface each tool uses

This is the step people skip, and it is why their setup half-works. Tools connect over one of two surfaces:

• Local server for desktop tools. Claude Code, Cursor, and Windsurf run on your machine, so they talk to a local MCP server, low latency, works offline.
• Remote endpoint for web tools. ChatGPT and hosted agents run in the cloud and cannot see a server on your laptop, so they need a hosted URL.

If you only set up a local server, your web tools read nothing. If you only set up remote, you give up the low-latency desktop path. You want both. The full tradeoff is in local vs remote MCP servers.

Step 3: Connect the desktop tools

For a desktop tool, the pattern is the same across the board: point the tool at a local MCP server in its settings or config, then restart the session so it picks up the server.

• Claude Code reads MCP servers from its config; add the server and reopen.
• Cursor and Windsurf have an MCP section in settings where you add the server.

Once it is connected, the tool lists the server's tools and resources, and the model starts using them when the task calls for it. You will know it worked when the next answer reflects your actual project instead of guessing.

Step 4: Connect the web tools

For a web tool, you add a remote MCP endpoint. The pattern is paste a URL, sign in, authorize. The tool then reaches that hosted server over the network for every session.

This is the step that brings ChatGPT and hosted agents into the same loop as your editor. No local install, just a URL and an auth step.

Step 5: Point everything at one shared context

Here is the step that separates a real setup from a pile of disconnected servers. Each tool can connect to MCP, but if Claude Code reads one source, Cursor reads another, and ChatGPT reads a third, you have rebuilt the silos MCP was supposed to remove. Three tools, three versions of the truth.

The fix is one shared context behind both the local bridge and the remote endpoint. Connect a new tool and it reads the same source as everything else, no re-curation.

And curate that context, do not dump into it. A raw export of every file you own makes the model wade through stale noise. The context worth connecting is structured and current: what is true (company, products, teams, projects, your area), what happened and what was decided, and what is next.

Step 6: Let write-back keep it current

The setup is not done when the connection works; it is done when it stays current on its own. The strongest pattern is read-and-write: tools read context at the start of a session and write a structured summary of activity, decisions, and tasks back as work happens. That way the next session, and the next teammate, start caught up instead of cold.

How BaseThread does all of this in one move

Doing steps 3 through 6 by hand, per tool, per person, is a chore that drifts. BaseThread collapses it.

A native Mac app provides the local bridge for desktop tools at low latency. The remote endpoint at mcp.basethread.ai serves web tools and hosted agents. Both read the same curated context graph and the same running record of activity, decisions, and tasks, so connecting Claude Code, Cursor, and ChatGPT means all three read one source. Integrations with tools like Notion and HubSpot distill the signal from connected systems into that context instead of dumping it in, so what your tools read is the relevant part, not the firehose. Your tools read it at session start and write activity, decisions, and tasks back as work happens. For a team, this is the difference between five private setups and one context layer, see MCP for teams and the team setup guide. BaseThread is in closed beta.

Related reading

What is MCP (Model Context Protocol)? A 2026 guide

MCP is an open standard that lets AI tools read outside context and call tools through one protocol. Here is what it is, how it works, and why it matters.

Local vs remote MCP servers: which your team needs

Local MCP servers run on your machine, remote ones are hosted at a URL. Here is how they differ, which tools each suits, and why teams usually want both.

MCP for teams: one context layer across your AI tools

MCP for teams turns scattered docs and decisions into one context layer every AI tool reads, so Claude Code, Cursor, and ChatGPT share the same source.

How to set up a shared MCP context server for your team

Set up a shared MCP context server your whole team's AI tools read: curate the context, choose local or remote, connect tools, scope access, and let it update.