How to share context between AI tools

The reliable way to share context between AI tools is to keep the source outside each tool. Put your background, decisions, and active work in one readable record. Then a Claude session, a Cursor session, or another supported client can begin from the same material instead of asking you to rebuild the brief.

The aim is simple: give every client a place to look before work starts.

Keep the source outside the chat

Each AI product has its own conversation history, memory rules, and project settings. That can work well inside the product you are using. It gets awkward when the work moves: you switch from a chat to an editor, use a different machine, or want a second assistant to pick up the same project.

Copying a context prompt between tools looks simple at first. Soon there are several versions. One has last week’s architecture decision. Another has the new constraint. You cannot tell which one is current until you read both.

Use a single source instead. It should be easy to edit when a fact changes, clear enough that you can inspect it yourself, and available to the clients that support your chosen connection. The goal is modest: the next session starts with the work you already did.

That source should hold only the context that travels. A chat’s rough thinking, a one-off question, or a client-specific setting can stay where it is. What’s worth keeping for your AI is a useful filter: save the things you would hate to explain again.

Give each kind of context a home

Shared context works better when the record says what applies where.

Your profile holds the facts that belong everywhere: your role, standing preferences, goals, writing style, and constraints. It should be short. A profile is a brief for someone about to work with you, not an autobiography.

Project context holds decisions and reference material for one piece of work. Keep the reason for a technical choice next to the project, rather than in a global instruction that every unrelated session has to carry.

Reusable procedures hold the steps that repeat across projects. A release checklist, API style guide, or research method belongs in a skill or guide that a compatible client can load when the task calls for it.

Task records hold what is happening now: the goal, status, dependencies, discussion, and result. They are useful when work crosses more than one session or person, because the current state is visible without reconstructing it from a transcript.

Those boundaries keep a shared record useful. Put everything in a profile and it becomes a long prompt nobody wants to maintain. Put every decision in a task comment and it becomes hard to find later. Give each fact the smallest home that fits.

Set up one shared record in vtriv

In vtriv, start in the web app with the Profile section in Settings. Write the handful of things a new assistant should know before it touches a project: what you do, the tools you prefer, the constraints you keep repeating, and the tone you want. The profile is private to you and stays in scope across your workspaces and MCP connections.

Next, create a project for work that has a life beyond one conversation. Add the design decision, source material, and open tasks there. If you chose Postgres because the system needs relational queries, write that reason down with the project. The next client can read the decision instead of reopening the same argument.

Then add the procedures that really repeat. A skill can hold the instructions for preparing a release or reviewing a pull request. You do not need a library on day one. One procedure you use twice is enough to prove the pattern.

Finally, connect the AI clients you use through vtriv’s hosted MCP connection. The app gives you the server URL and configuration in Settings; compatible clients open a browser sign-in through OAuth. The Claude setup guide has the client-specific walkthrough. Connection options and permissions differ by client, and ChatGPT’s MCP availability varies by plan, so a tool without a compatible MCP connection cannot read the record directly.

Try the handoff on purpose. Add a short decision from one connected client, then open a fresh session in another and ask it to orient itself before discussing the project. It should recover the profile and the relevant project material. If it cannot, fix the record or the connection before adding more context.

Let the record be the handoff

The useful test is not whether an assistant knows your favorite programming language. It is whether a fresh session can answer the question that used to cost ten minutes of setup.

Say you use Claude for planning and Cursor for implementation. The shared profile explains your preferences. The project document explains what you are building and why. A task says which migration is still open and includes the discussion from the last attempt. Cursor does not need Claude’s transcript. It needs the current record.

This also makes corrections cheaper. If a preference changes, update the profile once. If a decision changes, update the project document. The next connected session reads the corrected source. You are not hunting down old copies in several chats and hoping none survive.

The same shape helps when an external runner is involved. vtriv stores the task record and the surrounding context. The external runner decides whether to select the work, which client or model to use, and how to perform it. When the work is done, it can leave progress and a durable lesson in the same record for the next session. Persistent memory for long-running AI agents explains that handoff in more depth.

Where the handoff ends

A shared context layer preserves the record while each client keeps its own tools, capabilities, and connection permissions. Assignment records responsibility; a person, agent, or external runner decides what to do with it.

That is a useful limit. The record stays readable even when you replace a client, pause a project, or change the system that runs the work. Your operating system for context should outlast the assistant that happened to write the first draft.

Start with a profile and one active project. Move the few facts you keep typing into both places, connect the clients that can use the record, and see what the next fresh session already knows. Create your vtriv workspace when you are ready.