| 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 tools and resources via the MCP standard interface using stdio-based bidirectional communication. The server handles JSON-RPC 2.0 message framing, capability negotiation during initialization, and maintains protocol state across client connections. This enables any MCP-compatible client (Claude Desktop, custom agents, LLM applications) to discover and invoke server capabilities through a standardized protocol rather than direct API calls.
Unique: unknown — insufficient data on specific MCP implementation details (version support, protocol extensions, initialization flow specifics)
vs alternatives: Provides standardized MCP protocol compliance, enabling interoperability with any MCP-compatible client without custom adapter code
Provides a framework for defining tools with JSON Schema specifications, parameter validation, and execution handlers. Tools are registered with the MCP server and exposed to clients with full schema metadata (name, description, input schema, required parameters). When a client invokes a tool, the framework validates inputs against the schema, executes the corresponding handler function, and returns structured results. This decouples tool definition from tool execution, enabling dynamic tool discovery and type-safe parameter passing.
Unique: unknown — insufficient data on schema validation approach (JSON Schema library used, custom validation logic, error handling specifics)
vs alternatives: Standardizes tool definitions through JSON Schema, enabling automatic client-side UI generation and parameter validation without custom code per tool
Implements MCP resource protocol for exposing static or dynamic content (files, templates, documentation, configuration) to clients through a URI-based addressing scheme. Resources are registered with metadata (name, description, MIME type, URI) and can be read by clients without executing code. The framework handles resource discovery (listing available resources) and content retrieval (reading specific resource content), enabling clients to access shared context, templates, or reference materials without direct file system access.
Unique: unknown — insufficient data on resource implementation (dynamic vs static resources, caching strategy, content type handling)
vs alternatives: Provides standardized resource discovery and retrieval through MCP, eliminating need for separate documentation or knowledge base APIs
Enables definition of reusable prompt templates with variable placeholders that can be discovered and instantiated by MCP clients. Templates are registered with metadata (name, description, arguments schema) and clients can request template content with specific argument values. This allows centralizing prompt engineering on the server side while enabling clients (like Claude) to dynamically use optimized prompts without hardcoding them. Templates support argument validation and can reference other resources or tools.
Unique: unknown — insufficient data on template language, variable substitution approach, and argument validation mechanism
vs alternatives: Centralizes prompt management through MCP, enabling version control and optimization of prompts without client-side changes
Implements MCP initialization handshake that exchanges server and client capabilities, enabling feature detection and graceful degradation. During initialization, the server declares supported capabilities (tools, resources, prompts, sampling) and the client declares its capabilities. This allows servers to adapt behavior based on client features and clients to discover what functionality is available before attempting to use it. The negotiation happens once per connection and informs all subsequent interactions.
Unique: unknown — insufficient data on specific capability negotiation implementation and feature detection logic
vs alternatives: Enables interoperability across different MCP client implementations by standardizing capability advertisement and negotiation
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 smithery at 24/100. smithery 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
+1 more capabilities