| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification, exposing local resources and tools through a standardized bidirectional communication interface. The server handles MCP message routing, resource discovery, and capability advertisement using the protocol's JSON-RPC 2.0 transport layer, enabling Claude and other MCP-compatible clients to discover and interact with exposed resources without custom integration code.
Unique: Provides a quickstart template for MCP server implementation, reducing boilerplate for developers who want to expose resources via the official Model Context Protocol rather than building custom API wrappers
vs alternatives: Follows the official MCP specification for guaranteed compatibility with Claude and future MCP clients, whereas custom REST APIs require individual integration work for each client
Defines and advertises available resources through MCP's resource schema mechanism, allowing clients to discover what data, files, or services are available before making requests. The server maintains a resource registry that describes each resource's URI pattern, MIME type, and metadata, which clients query via the resources/list endpoint to build dynamic UI or determine what operations are possible.
Unique: Implements MCP's resource advertisement pattern, enabling declarative resource discovery where clients query available resources via a standard endpoint rather than relying on documentation or hardcoded knowledge
vs alternatives: Provides automatic resource discovery through MCP's standard mechanism, whereas REST APIs typically require separate OpenAPI/Swagger documentation that clients must parse independently
Exposes callable tools or functions through MCP's tools interface, allowing clients to discover available operations and invoke them with validated parameters. The server defines tool schemas (name, description, input schema) that clients use to understand what operations are available and what parameters they accept, then routes tool invocation requests to handler functions with automatic parameter validation and error handling.
Unique: Implements MCP's tools interface for function exposure, providing schema-based tool discovery and invocation that works across any MCP-compatible client without custom integration code
vs alternatives: Standardizes tool exposure through MCP rather than building custom function-calling APIs, enabling compatibility with multiple clients and reducing integration friction
Handles bidirectional message routing between MCP clients and server handlers using JSON-RPC 2.0 over the MCP transport layer (typically stdio or HTTP). The server maintains request/response correlation, handles async message delivery, manages connection state, and routes incoming client requests to appropriate handler functions while sending server-initiated notifications back to clients.
Unique: Implements the full MCP message routing stack with proper request/response correlation and bidirectional notification support, abstracting transport complexity from resource and tool handlers
vs alternatives: Provides standardized message routing through MCP rather than custom request/response handling, ensuring compatibility with the protocol specification and reducing implementation bugs
Provides a quickstart template or example implementation that developers can fork or extend to build their own MCP servers. The template includes basic server setup, resource and tool definitions, handler stubs, and configuration examples, reducing the time needed to create a working MCP server from scratch and establishing best practices for server structure and error handling.
Unique: Provides a ready-to-use MCP server template that developers can immediately fork and customize, reducing setup time and establishing consistent patterns for MCP server implementation
vs alternatives: Offers a concrete working example rather than just protocol documentation, enabling developers to start building MCP servers in minutes rather than hours of reading specifications
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs quickstart-resources at 24/100. quickstart-resources leads on ecosystem, while Supabase is stronger on quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities