| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides a standardized MCP server factory that handles initialization, request routing, and graceful shutdown. The core exports server construction patterns that abstract away protocol-level details, allowing developers to focus on tool and resource definitions rather than managing WebSocket/stdio transports, message serialization, or error propagation across the MCP specification.
Unique: Provides opinionated MCP server scaffolding with built-in patterns for tool registry and request routing, reducing boilerplate compared to raw MCP SDK usage while maintaining full protocol compliance
vs alternatives: Faster to production than implementing MCP servers from scratch with the raw SDK, with less protocol-level complexity than building custom RPC frameworks
Implements a centralized registry that maps tool definitions (name, description, input schema) to handler functions, with automatic JSON Schema validation and type coercion. Tools are registered declaratively with OpenAI-compatible function calling schemas, and the registry handles parameter validation, error wrapping, and result serialization before returning to the MCP client.
Unique: Combines declarative tool registration with automatic JSON Schema validation and OpenAI-compatible function calling format, eliminating manual schema-to-function mapping boilerplate
vs alternatives: More structured than ad-hoc tool registration, with built-in schema validation that catches parameter mismatches before execution, unlike raw function arrays
Defines a pluggable KV interface that abstracts storage backends (in-memory, Redis, DynamoDB, etc.) behind a consistent get/set/delete/list API. The interface uses async/await patterns and supports TTL, batch operations, and optional namespacing, allowing servers to persist state without coupling to a specific storage implementation.
Unique: Provides a minimal, async-first KV interface that decouples MCP servers from storage implementation, with optional namespacing for multi-tenancy without requiring a full database abstraction layer
vs alternatives: Simpler than full ORM/database abstractions while still enabling backend flexibility, with explicit async patterns that match MCP's async request handling
Exposes a Host interface that provides runtime context about the execution environment (platform, available resources, client capabilities, configuration). The server can query host properties to adapt behavior (e.g., enable/disable features based on client support), and the interface supports lazy initialization of expensive resources like database connections.
Unique: Provides a Host abstraction that enables servers to query runtime capabilities and adapt behavior without hardcoding environment assumptions, with lazy resource initialization to minimize startup overhead
vs alternatives: More flexible than environment variable configuration alone, allowing servers to adapt to client capabilities and resource availability at runtime without code changes
Defines an Auth interface for pluggable authentication strategies (API keys, OAuth, mutual TLS, custom schemes) with support for token validation, permission checking, and audit logging. The interface integrates with the tool registry to enforce per-tool access control, allowing servers to implement fine-grained authorization without modifying tool handlers.
Unique: Provides a pluggable Auth interface that integrates with the tool registry for declarative per-tool access control, enabling multi-tenant MCP servers without modifying tool implementations
vs alternatives: More granular than simple API key validation, supporting multiple auth strategies and per-tool permissions while remaining decoupled from tool logic
Implements a middleware chain pattern for intercepting and transforming MCP requests and responses. Middleware can validate requests, enrich context, transform payloads, log events, or enforce policies before tools execute. The pipeline is composable and supports both sync and async middleware, with error handling that propagates failures back to the MCP client.
Unique: Provides a composable middleware pipeline integrated with the MCP request lifecycle, supporting both sync and async middleware with shared context propagation and error handling
vs alternatives: More flexible than per-tool decorators, allowing cross-cutting concerns to be applied uniformly across all tools without modifying tool code
Enables servers to expose resources (documents, files, data streams) alongside tools, with support for streaming large payloads and content negotiation. Resources are defined with URIs and MIME types, and the interface handles chunked delivery to MCP clients, allowing tools to reference and manipulate resources without loading entire contents into memory.
Unique: Integrates resource streaming with the tool registry, allowing tools to declare dependencies on resources and MCP clients to access them via URI without coupling to file system or storage implementation
vs alternatives: More efficient than embedding large payloads in tool responses, with streaming support that prevents memory exhaustion on large files
Provides standardized error handling that converts exceptions and validation failures into MCP-compliant error responses with structured error codes, messages, and optional context. The system distinguishes between client errors (invalid parameters), server errors (tool execution failures), and protocol errors, with proper HTTP-like status codes and error serialization.
Unique: Provides MCP-compliant error handling with structured error codes and context propagation, distinguishing between client/server/protocol errors without requiring manual error wrapping in tool code
vs alternatives: More structured than generic exception handling, with MCP-specific error serialization that ensures clients receive properly formatted error responses
+1 more capabilities
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 @exitmcp/core at 22/100.
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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