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MCP Server Function

The mcp-server function implements a Model Context Protocol (MCP) server that enables communication between AI models and external systems.

Overview

The MCP server provides a standardized way for AI models to access external resources and tools through a well-defined protocol. It acts as a bridge between AI models and various data sources, APIs, and system capabilities.

Features

  • Resource access: Provides access to database, storage, and system resources
  • Tool execution: Enables AI models to execute tools for database operations, messaging, and system tasks
  • Authentication: Secures access to resources and tools
  • Realtime communication: Supports WebSocket-based communication for interactive sessions

Architecture

The MCP server is built with a modular architecture:

  • Core server implementation
  • Resource registry and handlers
  • Tool registry and handlers
  • Authentication and security
  • Error handling utilities

Environment Variables

Variable Description Default
SUPABASE_URL Supabase project URL Required
SUPABASE_SERVICE_ROLE_KEY Supabase service role key Required
MCP_AUTH_SECRET Secret for authenticating MCP requests Required

API Usage

Resources

Resources represent data sources that can be accessed by AI models:

  • Database resources: Access to Supabase database tables
  • Storage resources: Access to Supabase storage buckets
  • System resources: Access to system information and configuration

Tools

Tools represent executable functions that AI models can invoke:

  • Database tools: Query, insert, update, and delete database records
  • Messaging tools: Send messages to users or other systems
  • System tools: Execute system operations and retrieve information

Testing

The MCP server includes comprehensive tests:

  • Unit tests for core components
  • Integration tests for end-to-end functionality
  • Agent tests for simulating AI model interactions

Deployment

The MCP server can be deployed as a Supabase Edge Function, making it accessible to AI models through HTTP requests.

Security Considerations

  • All requests must be authenticated
  • Access to sensitive resources and tools is controlled
  • Input validation prevents injection attacks
  • Error handling avoids leaking sensitive information