How to sync context across AI tools without copying it everywhere

Stop giving each tool its own copy. Keep the context in one place you control and expose it through an MCP server that assistants read from when they need it. Nothing is synced because nothing is duplicated. The part that fails most often is permissions: whatever reads the context must inherit the person asking, not a service account that can see everything.

JarvisBitz

You have a project brief in one assistant, a set of decisions in another, and a codebase that a third one has been reading all week. Now you want the fourth tool to know all of it, and the only way you have found is to paste the same summary into each one and hope they do not drift apart. If you are searching for a way to sync that context between AI platforms automatically, here is the honest answer first: there is no button for it, and there is unlikely to be one, because the vendors have no reason to build a bridge out of their own product. What does exist is a pattern that solves the problem properly, and it is less work than the copy and paste you are doing now.

Why the obvious fixes stop working

Every team tries the same three things, in the same order, and they fail for the same reason: each one makes a copy, and copies drift.

• Paste a summary into each tool. It works for a day. Then a decision changes in one place, and three tools are now confidently working from the old version. Nobody notices until something is built on it.
• Use each tool's memory feature. Most assistants now remember things about you. None of them share that memory with another vendor's assistant, and none will. Per tool memory is a moat, not a bridge.
• Export and re-import. Now your data lives in three places with three different formats, and the export is stale the moment it lands.

The mistake underneath all three is treating the context as something each tool should own. The moment two tools each own a copy, you have a synchronisation problem, and synchronisation problems are never finished.

The pattern that holds: one source of truth, read on demand

Flip it. The context lives in exactly one place that you control, and every tool reads from it when it needs to, instead of keeping its own copy. Nothing is synced because nothing is duplicated. When the brief changes, it changes once, and the next question any tool asks gets the current answer.

Two years ago this required a custom plugin for every assistant. Now there is a shared way to do it. The Model Context Protocol, or MCP, is an open standard that lets an AI tool ask an external server for information or actions in a form the tool understands. Claude, ChatGPT, Cursor, VS Code, and a growing list of others can connect to an MCP server. You write, or have written, one server that exposes your context. Each tool connects to that server. That is the whole architecture.

What the server actually exposes

An MCP server offers three kinds of thing, and getting the split right is most of the design work.

• Resources. Documents the tool can read: the current project brief, the decision log, the architecture notes, the glossary of terms your team uses. These are read only and always current, because they are served from wherever they actually live (a wiki, a repository, a database), not from a copy.
• Tools. Actions the assistant can take: look up a ticket, search the decision log for a keyword, fetch the latest version of a spec. Each one is a small, named, bounded operation, which is what makes it safe to hand to a model.
• Prompts. Reusable instructions: "summarise this in our house style", "review against our API conventions". These are how you stop every tool from needing the same 400 words of preamble pasted in.

Just as important is what the server refuses to expose. It should not hand over everything it can reach. A context server that returns the whole wiki to every question is a slow way to fill a context window with noise, and a fast way to leak something that should not have left the building.

Permissions, which is where most attempts actually fail

Here is the part that gets skipped and then hurts. When an assistant reads from your context server, whose permissions does it use? The easy answer is a service account that can see everything, and the easy answer is wrong. It means a junior contractor asking an assistant a question can, through the assistant, read the board pack.

The rule is that whatever reads the context inherits the person who is asking, not a blanket identity. The server knows who the user is, checks what that user may see, and answers only from that. This is the same discipline that decides whether a retrieval system leaks documents or not, and it has to be built in at the server, because no assistant on the other end will enforce it for you.

Where it is still genuinely hard

• Tools with closed memory. Some assistants let you connect a server for documents but keep their own conversational memory sealed. You cannot read that memory out. The practical answer is to stop relying on it: anything worth remembering gets written to the source of truth by a person or a tool call, not left inside one vendor's box.
• Keeping the source of truth current. The architecture removes copies; it does not remove the need for someone to update the original. If the decision log is three weeks stale, every tool is now uniformly, consistently wrong. That is still better than inconsistently wrong, but it is not good.
• Rate limits and cost. A tool that fetches the full context on every message will hit vendor limits and burn tokens. The server should return the slice that answers the question, and the tools should be told, in their instructions, to ask for what they need rather than everything.
• Several agents, not several tools. Once it is agents talking to agents rather than people talking to assistants, the question becomes routing and hand off, which is orchestration and a different article.

If you have an afternoon, versus if this matters

An afternoon. Put the context in one plain text or markdown file in a repository. Point the tools that support it (most coding assistants do) at that file. Delete every other copy. You have not built a server, but you have removed the duplication, which is most of the win.

If this matters. Build the MCP server. Start with resources only, no tools, no prompts: three or four documents served read only, with per user permissions from day one. Connect two assistants. Live with it for two weeks. Then add the tool calls that people actually asked for, and not the ones that seemed clever. Teams that do it in this order end up with a small server that is used constantly; teams that start with twenty tools end up with a large server that nobody trusts.

If you would rather have it built than build it, this is the kind of integration work we do, and the permissions layer is usually where we earn the fee.

Related reading

LLM data residency: what to configure and what to verify

Residency is a configuration you set, not a property you inherit, and the defaults differ per vendor and change without notice. Establish four things in writing for each provider, then enforce the rest at your own gateway, because that is the only layer you control.

LLM spend: attribution before optimisation

You cannot cut what you cannot see. Most teams optimise models and prompts before they can attribute a single dollar to a team, feature or customer, which means they are guessing. Meter at the gateway first, then cache, route and cap in that order.

What to log in an LLM system so you can debug it later

A 200 response tells you almost nothing about an AI system, because the failure you care about is a confident wrong answer that returned successfully. Log the inputs, the retrieved context, every tool call and the guardrail outcomes, or you will be debugging from screenshots.