| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a Model Context Protocol (MCP) server that manages bidirectional communication between AI clients and external tools/resources. The server handles protocol initialization, message routing, resource discovery, and graceful shutdown using the MCP specification's standardized message format and transport layer abstraction.
Unique: unknown — insufficient data on gfhf's specific implementation approach, architecture patterns, or how it differs from other MCP server implementations
vs alternatives: unknown — insufficient data to compare against alternative MCP server frameworks or implementations
Provides a mechanism to define tools with JSON Schema-based parameter specifications and register them with the MCP server for discovery by AI clients. The system validates incoming tool invocations against declared schemas before execution, ensuring type safety and preventing malformed requests from reaching tool handlers.
Unique: unknown — insufficient data on gfhf's specific schema validation implementation, whether it uses standard JSON Schema libraries or custom validation logic
vs alternatives: unknown — insufficient data to compare schema validation approach against other MCP server implementations or tool frameworks
Enables registration of resources (files, data, or computed content) that can be read or listed by MCP clients through standardized resource endpoints. Resources are identified by URI-like paths and served with metadata (MIME type, size, modification time), allowing AI clients to access application data without direct filesystem or API access.
Unique: unknown — insufficient data on gfhf's resource implementation, whether it supports streaming, caching, or special handling for different content types
vs alternatives: unknown — insufficient data to compare resource serving approach against REST APIs or other MCP resource implementations
Implements JSON-RPC 2.0 message handling with request ID tracking to correlate responses with requests across asynchronous communication channels. The system routes incoming messages to appropriate handlers, manages message queues, and ensures responses are delivered to correct clients even in high-concurrency scenarios.
Unique: unknown — insufficient data on gfhf's specific message routing implementation, concurrency model, or how it handles backpressure and message queuing
vs alternatives: unknown — insufficient data to compare message routing approach against other MCP server implementations or message queue patterns
Provides pluggable transport implementations allowing MCP servers to communicate via different protocols (stdio, TCP, WebSocket, HTTP) without changing core server logic. Transport abstraction handles protocol-specific framing, connection lifecycle, and serialization while maintaining uniform message handling at the application layer.
Unique: unknown — insufficient data on gfhf's specific transport abstraction design, which protocols it supports, or how it handles protocol-specific edge cases
vs alternatives: unknown — insufficient data to compare transport abstraction against other MCP server frameworks or protocol abstraction patterns
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 gfhf at 22/100. gfhf 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