Supabase MCP Server on Phala Cloud

The Supabase MCP Server brings the power of Supabase’s real‑time database and authentication services into a trusted execution environment (TEE) on Phala Cloud. By running the MCP server inside a secure enclave, developers can expose Supabase’s rich data APIs to AI assistants while ensuring that all data handling remains confidential and tamper‑proof. This solves a critical pain point for teams that need to integrate cloud databases with generative AI workflows without exposing sensitive data to the public internet or relying on third‑party gateways.

At its core, the server implements the MCP specification to expose Supabase resources—tables, functions, and authentication endpoints—as MCP tools. These tools can be invoked by an AI assistant to perform CRUD operations, trigger serverless functions, or query real‑time streams. The integration is seamless: the MCP client sends a JSON payload describing the desired action, and the server forwards it to Supabase’s REST or GraphQL endpoints inside the enclave. Because all traffic stays within the Phala TEE, developers retain full control over access policies and can audit every interaction with the database.

Key capabilities include:

• Secure data access through Phala’s TEE, protecting credentials and query results from external inspection.
• SSE (Server‑Sent Events) transport that allows the MCP server to push real‑time updates from Supabase to the AI assistant, enabling live dashboards or dynamic content generation.
• Docker‑ready deployment on Phala Cloud, so teams can spin up the server with a single configuration file and scale it alongside other microservices.
• Built‑in compatibility with the existing supabase-mcp implementation, ensuring that any tool or prompt defined for Supabase can be leveraged without modification.

Real‑world scenarios abound. A financial services firm could let an AI assistant generate instant portfolio reports by querying a Supabase database that stores transaction data, all while the TEE guarantees compliance with privacy regulations. A healthcare startup could expose patient records to an AI chatbot for triage, confident that the data never leaves the enclave. Even a marketing team might use the server to pull live campaign metrics and have an assistant draft personalized outreach messages on the fly.

Integrating this MCP server into an AI workflow is straightforward: developers configure the Phala Cloud deployment, expose the SSE endpoint, and then point their MCP client (or a higher‑level AI framework) to that URL. From there, the assistant can treat Supabase as a first‑class tool—calling , , or subscribing to —without writing custom adapters. The result is a secure, scalable bridge between cloud data services and conversational AI that preserves confidentiality while unlocking powerful automation.