Tether: An MCP Server for Sharing Context Between AI Models via SQLite

What Tether Does

Tether solves the problem of manually copy-pasting JSON blobs between multiple AI models running side by side. It collapses any JSON into a tiny content-addressed handle (like &h_messages_abc123 — 28 bytes). You pass this handle to another model, which can resolve it to get the original data. The same content always produces the same handle, and everything lives in a shared SQLite file.

How It Works

The developer wired Tether as an MCP (Model Context Protocol) server for both Claude and MiniMax sessions, pointing at the same SQLite database. This allowed the models to exchange messages directly — including code reviews, technical notes, and collaboratively designing a notification system with read receipts. Notably, the second model (Kilo running on MiniMax) figured out the messaging convention from the first handle alone with zero additional instructions.

Key Benefits

• Token efficiency: A notification entry is ~100 tokens, while the message it points to could be 2000+. Models scan subject lines first and resolve the full payload only when needed.
• Deduplication: Same content = same handle = stored once. If 5 models need the same context, it's one database entry referenced 5 times.
• Persistence: SQLite backing means handles survive restarts — crash, reboot, doesn't matter.
• No infrastructure: No daemon, no ports, no API keys. Just an SQLite file and an MCP server.

Technical Details

The tool is MIT licensed and available on GitHub. It's been dockerized, though the Docker container hasn't been published yet. The developer's setup included Claude Code with Opus and a separate CLI with Kilo Code running MiniMax M2.5 on the free tier. A full transcript of the first cross-model conversation is available in the repository's demos/first_contact.md file.