My Ultimate Setup for Sharing Context Across AI Coding Tools

A lot of users, including myself, who rely on AI chatbots and coding agents for business and development workflows share one major concern: the cost and speed of token consumption.

I use three chatbots for daily work: ChatGPT, Claude, and Gemini. The common problem is that what ChatGPT knows about me does not carry over to Claude or Gemini. I end up repeating the same information multiple times.

In my development workflows, I switch between Warp, Claude Code, and Codex. The biggest challenge is the number of tokens wasted by rebuilding the project context in each environment.

Founders and developers have long asked for a self-updating context layer for AI. Finally, we have one. It is called Unabyss.

What is Unabyss?

Unabyss is a personal context layer for your AI.

You connect your apps once, and Unabyss extracts and organizes your role, company, voice, and decisions into a vault that any AI tool can access through MCP.

There is no need to re-explain yourself to every new chatbot, agent, or IDE. You can also connect sources such as LinkedIn, X, Gmail, Notion, Slack, Google Drive, Calendar, GitHub, and more.

Unabyss chat interface. Image by Jim Clyde Monge

An extraction engine structures the information into clean, layered files like persona.md, voice.md, and company.md. These files are optimized for LLMs and agents. The vault updates automatically as your connected tools sync, keeping your context current without manual effort.

Unabyss tries to make this less messy by turning your connected data into a living context layer that your AI tools can pull from when needed.

What’s great about this new platform is that you don’t need to pay an upfront fee. New users receive $20 in free credits on signup, with no card required. After that, it uses pay-as-you-go pricing.

Note: Unabyss is live on Product Hunt today. You can check the launch here.

How Unabyss Works

Connect Your Sources

Start by connecting the apps you use most. Unabyss supports popular platforms like X, LinkedIn, GitHub, Gmail, Notion, Slack, and Google Drive. Only link the sources you are comfortable sharing.

Go ahead and link all platforms you’re comfortable connecting.

Unabyss connector apps. Image by Jim Clyde Monge

Go ahead and link the platforms that make sense for your workflow. If you mainly want Unabyss to understand your public profile and writing style, connecting LinkedIn and X may already be useful.

If you want it to understand your development work, GitHub will obviously matter more. If you want it to help with meetings, planning, or work history, tools like Gmail, Calendar, Drive, and Notion become more useful.

For the GitHub connection, you need to manually choose the repositories that will be imported into the context.

Unabyss GitHub configuration screen. Image by Jim Clyde Monge

I won’t recommend syncing everything because it’ll bloat the context and slow down the syncing. Just choose the one you think needs to be synced.

For files not on cloud services, such as PDFs, notes, or local documents, you can upload them directly into the Sources dashboard. Unabyss processes these files to integrate their information into your overall context.

Unabyss context sources. Image by Jim Clyde Monge

If there is a file that’s not on your Google Drive or it’s a favorite book that you have in PDF or whatever format, you can just upload it in here and it’s going to be funneled into your context as well.

What happens underneath is that Unabyss structures and organizes all the information about you. It connects facts, pulls useful details from your sources, and builds a fuller context of your identity, work, projects, and preferences.

Now let’s try the chatbot to see if it’s able to extract data from the apps I’ve connected. Here’s a sample response when asked who am I.

Unabyss chat examples. Image by Jim Clyde Monge

Perfect! The model was able to combine information about me from both LinkedIn and X. This is a lot more accurate than ChatGPT or Gemini alone because it is not only relying on past conversations. It is pulling from a structured context created from the apps I connected.

Connecting to Claude via MCP

The easiest way to connect to Unabyss is through OAuth.

Open a terminal and execute the following command:

claude mcp add --transport http unabyss https://mcp.unabyss.com/

This will add the unabyss to your local MCPs, but notice that it requires authentication.

Connecting to Unabyss via OAuth. Image by Jim Clyde Monge

Hit enter and select the Authenticate option in the next screen.

Connecting to Unabyss via OAuth. Image by Jim Clyde Monge

This will open a browser window where you need to explicitly approve the authentication request. You can also adjust the connection name and the set of permissions in this modal window.

Connecting to Unabyss via OAuth. Image by Jim Clyde Monge

If all looks good, click on the Approve button and wait until you see the success message. Close the browser window and notice that an authentication success message is displayed in the terminal.

Connecting to Unabyss via OAuth. Image by Jim Clyde Monge

Another way to connect to Unabyss is via a bearer token. Open MCP tokens and create a long-lived bearer for the app or agent you’re connecting.

Unabyss MCP token generation. Image by Jim Clyde Monge

Now that you have the token, the next step is to connect it to your favorite agentic development environment. Unabyss supports some of the most popular ADEs out there, like Claude Code, Cursor, OpenClaw, Codex, ChatGPT, OpenCode, Claude, and more.

Unabyss supported apps. Image by Jim Clyde Monge

For Claude Code, there are two ways to do this.

The first option is to add the MCP through Claude. Open a terminal and run the following command:

# Add Unabyss as an HTTP MCP for Claude Code
claude mcp add --transport http unabyss https://mcp.unabyss.com/ \
  --header "Authorization: Bearer <your-mcp-token>"

Unabyss Claude Code example. Image by Jim Clyde Monge

This adds Unabyss as an HTTP MCP server with a bearer token header. Restart any open Claude Code sessions after running the command.

To confirm that the MCP has been successfully installed, type /mcp in your terminal, check if the /unabyss option is there.

Unabyss Claude Code MCP connection verification. Image by Jim Clyde Monge

Another way is to implement a universal mcpServers JSON configuration. Hosts that read an mcpServers config block can use this directly, which is helpful if you want to share the same setup across machines.

{
  "mcpServers": {
    "unabyss": {
      "url": "https://api.unabyss.com/mcp/",
      "headers": {
        "Authorization": "Bearer <your-mcp-token>"
      }
    }
  }
}

The final thing to do is add the system prompt into CLAUDE.md. Here’s the full text:

## Unabyss — User Context Layer

You have access to Unabyss, the user's persistent context layer across AI tools. It knows their background, role, projects, preferences, prior decisions, and connected data sources.

**Session start:**
- Call `unabyss.whoami` once to load baseline context about the user (name, role, key facts) — same cost as a single `query`.
- Call `unabyss.list_integrations` to see what data sources the user has connected. If a task needs an app that isn't connected, call `unabyss.propose_connection` instead of degrading the answer.
- Call `unabyss.export_list` to see prebuilt context exports. Any export flagged as changed since last read is worth reading via `unabyss.export_read`.

**Reading context:**
- `unabyss.query` — fast lookups. Use when you need a specific fact the user has already shared ("what's my stack", "what's my role").
- `unabyss.agentic_query` — deep reasoning. Use for synthesis ("how would I approach X", "what would I think about Y"). If it returns a `query_id`, poll `unabyss.agentic_query_read` after the suggested delay.

**Writing context:**
- Call `unabyss.store` whenever the user states a preference, makes a decision, mentions a tool they use, names a project, or shares any durable fact. Err on the side of storing — persistence across tools is the entire point.
- When you learn significant new identity facts about the user (new role, project, location), propose an `unabyss.update_identity` change — show the proposed summary and wait for explicit confirmation before calling. Never write the identity summary without asking first.

**Exports:**
- `unabyss.export_create_from_text` — instant and free. Use when the user asks to save a response, summary, or analysis as an export.
- `unabyss.export_create` and `unabyss.export_refresh` are expensive multi-minute generations. Only call when the user explicitly asks for a new or refreshed export.

Treat Unabyss as the source of truth for personal context. Your session memory is not.