Think inside your AI world.
How do I connect Supabase to ChatGPT?
The backend over MCP answers ChatGPT in feature groups — database, project management, config — with 29% of surveyed startups already running MCP in production.
Direct answer: connect Supabase's MCP server to ChatGPT. In ChatGPT, add it from the connector settings (developer mode); the tool's own docs carry the current path. Authorised, ChatGPT works the grouped surface — schema and queries, project management, configuration — to the extent you grant it.
Backend questions from the thinking seat
What the schema holds, how many rows match, which settings drifted between environments — answered against the live backend from whichever assistant hosts the thinking.
Security defaults are decisions
Worked case: you ratified an RLS rule — no new table ships without row-level security reviewed, ever since a public-by-default table spent a weekend readable by anyone with the anon key. Supabase makes RLS easy; making it mandatory was your call, recorded in no migration.
Through Unl the schema read arrives with the mandate applied: one new table this week, RLS unreviewed — blocked from shipping by your rule, the readable-weekend story attached. The check runs at read time, before the deploy conversation gets casual.
Supabase to ChatGPT reads and manages the backend by grant; through Unl the backend answers to the security mandates you ratified — the RLS review happens because you decided it always does.
Reads through Unl arrive with measured context — in the presence of the decisions you’ve already settled. The reach lane is live: one box, paste anything. If it speaks MCP, Unl can reach it. Readings arrive unprompted, the data beside the criterion; Unl is a courier, not a warehouse, and keeps only your keys and the frame.
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Questions people ask
What are the Supabase MCP feature groups?
Database (schema, queries), project management, and configuration — grantable in slices, so the assistant holds only what you are comfortable delegating.
Is MCP-to-backend really production-common?
Supabase's own State of Startups put it at 29% in production, 28% experimenting — the pattern is mainstream among its users.
Does Unl touch the database?
Not while measuring — a read only reads, so holding a migration up against your ratified rules never touches anything. Past that it can: where a server exposes a tool that writes, Unl can call it, because the socket exposes a server's full surface rather than a chosen subset, and that call carries your explicit in-turn gesture, verified at the choke point. Supabase's feature groups are grantable in slices as well, so you can withhold the write surface at authorisation and never reach the question.
What this is
Think inside your AI world — you stay in command
Unlimitless (Unl to friends) holds what you've settled, reads what your tools are showing, and catches what's changed out in the world — and hands your AI whatever bears on the work, the moment it's needed, without you asking. The right thing, in front of the model, unprompted, with you in command of the call. So you keep moving toward what you set out to build, on top of everything you've already decided.
It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector. Quick to connect, in a couple of steps.
Unlimitless is open now to invited Alpha. Apply for the Beta waitlist to come in ahead of the full launch:
Alpha is invite-only · free at launch.