| 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, enabling Claude and other MCP-compatible clients to discover and invoke tools through a standardized JSON-RPC 2.0 interface. The server exposes capabilities via JSON Schema definitions, allowing clients to understand tool signatures, parameters, and return types without hardcoded knowledge of specific implementations. Uses bidirectional message passing over stdio or HTTP transports to maintain persistent tool availability across conversation sessions.
Unique: Implements the MCP server-side specification, providing a standardized interface for tool exposure that decouples tool implementation from client logic through schema-based capability advertisement and JSON-RPC message routing.
vs alternatives: Follows an open protocol standard (MCP) rather than proprietary APIs, enabling interoperability across multiple LLM clients and reducing vendor lock-in compared to OpenAI function calling or Anthropic-specific integrations.
Exposes available tools to MCP clients through structured JSON Schema definitions that describe tool names, descriptions, required/optional parameters, parameter types, and return value schemas. Clients parse these schemas to understand what tools are available and how to invoke them without requiring out-of-band documentation. The server maintains a registry of tool definitions that clients query during initialization and can refresh on demand, enabling dynamic tool availability.
Unique: Uses JSON Schema as the canonical format for tool capability advertisement, enabling clients to introspect tool signatures and validate parameters before invocation, rather than relying on string-based documentation or hardcoded tool knowledge.
vs alternatives: More flexible and extensible than OpenAI's function calling schema format because it supports arbitrary JSON Schema constraints and enables client-side validation before tool invocation, reducing round-trip errors.
Routes incoming JSON-RPC 2.0 requests from MCP clients to appropriate tool handlers and returns structured responses following the JSON-RPC specification. Implements request/response correlation using message IDs, error handling with standardized error codes, and support for both synchronous tool execution and asynchronous result delivery. The routing layer abstracts transport details (stdio, HTTP, WebSocket) so tool implementations remain transport-agnostic.
Unique: Implements full JSON-RPC 2.0 message routing with ID-based request correlation and transport abstraction, allowing tool handlers to remain independent of the underlying communication mechanism (stdio, HTTP, WebSocket).
vs alternatives: More robust than simple function call forwarding because it provides standardized error handling, request correlation, and transport flexibility, compared to ad-hoc REST API approaches that require custom error handling and correlation logic.
Abstracts the underlying communication transport layer so MCP servers can operate over stdio (for local Claude integration), HTTP (for remote clients), or WebSocket (for persistent bidirectional connections). The server implementation handles transport-specific details like message framing, connection lifecycle, and protocol negotiation, exposing a unified interface to tool handlers. This enables the same tool implementation to be deployed across different transport mechanisms without modification.
Unique: Provides pluggable transport layer that abstracts stdio, HTTP, and WebSocket details from tool implementation, enabling the same tool code to be deployed across different communication mechanisms through a unified interface.
vs alternatives: More flexible than single-transport solutions because it supports local (stdio), remote (HTTP), and persistent (WebSocket) deployments from the same codebase, compared to tools locked into specific transports like REST-only APIs.
Validates incoming tool invocation parameters against declared JSON Schema definitions before execution, ensuring type correctness and constraint satisfaction. Performs type coercion where appropriate (e.g., string-to-number conversion) and rejects invalid parameters with detailed error messages indicating which constraints were violated. This validation layer prevents malformed requests from reaching tool handlers and provides clients with immediate feedback on parameter errors.
Unique: Implements JSON Schema-based parameter validation at the MCP protocol layer, catching invalid parameters before they reach tool handlers and providing structured error responses that clients can parse and act upon.
vs alternatives: More comprehensive than runtime type checking in tool handlers because it validates all constraints (min/max, pattern, enum, etc.) upfront and provides standardized error responses, compared to ad-hoc validation scattered across tool implementations.
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 aayushnaphade at 24/100. aayushnaphade leads on ecosystem, while Supabase is stronger on quality.
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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
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