| Ecosystem | 0 | 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 through a standardized JSON-RPC 2.0 interface that allows Claude and other MCP-compatible clients to discover and invoke capabilities. The server handles protocol negotiation, capability advertisement, and bidirectional message routing between client and server implementations.
Unique: Implements MCP server specification with intent-based tool routing, allowing semantic discovery and invocation of capabilities rather than requiring explicit endpoint knowledge
vs alternatives: Provides standardized protocol-based tool exposure vs. custom REST APIs or direct function bindings, enabling interoperability across MCP-compatible clients without reimplementation
Analyzes incoming requests to extract user intent and semantically matches them against available tools using natural language understanding rather than exact string matching. The server likely uses embedding-based or LLM-based intent classification to route requests to the most appropriate tool implementation, enabling fuzzy matching and multi-step intent resolution.
Unique: Uses intent-based routing rather than explicit tool name matching, enabling semantic understanding of user requests and automatic tool selection based on intent similarity
vs alternatives: More flexible than static tool registries because it understands intent semantically, reducing friction when users don't know exact tool names or phrasing
Exposes a standardized interface for clients to discover available tools, their parameters, return types, and usage documentation. The server maintains a registry of tools with JSON Schema definitions for input validation and output typing, allowing clients to introspect capabilities and generate appropriate requests without out-of-band documentation.
Unique: Implements MCP-compliant tool discovery with full JSON Schema support, enabling clients to understand tool contracts and validate invocations before execution
vs alternatives: More robust than documentation-based tool discovery because schemas are machine-readable and enable automatic validation, reducing runtime errors from malformed requests
Provides a resource abstraction layer that allows clients to request contextual information (documents, code snippets, configuration, etc.) through a standardized read/list interface. Resources are identified by URI and can be streamed or returned in full, enabling clients to build context for tool invocations without embedding all data in tool parameters.
Unique: Implements MCP resource abstraction with URI-based addressing, allowing clients to fetch contextual information on-demand without embedding all data in tool parameters
vs alternatives: More scalable than embedding all context in requests because resources are fetched on-demand, reducing token usage and enabling access to large knowledge bases
Manages JSON-RPC 2.0 message exchange between MCP client and server, handling request/response correlation, error propagation, and protocol-level exceptions. The server implements timeout handling, malformed request detection, and graceful degradation when tools fail, ensuring robust communication even under adverse conditions.
Unique: Implements full JSON-RPC 2.0 protocol with MCP-specific error handling, including request correlation, timeout management, and graceful degradation for tool failures
vs alternatives: More robust than simple request-response patterns because it handles protocol-level errors, timeouts, and malformed requests without dropping client connections
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 catchintent at 22/100. catchintent 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