| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a fully-functional MCP server that handles the Model Context Protocol handshake, message routing, and bidirectional communication with MCP clients. The server manages connection lifecycle including initialization, resource discovery, and graceful shutdown, using the standard MCP message format for request-response and notification patterns.
Unique: unknown — insufficient data on specific transport implementation, message handling patterns, or architectural decisions differentiating this MCP server from reference implementations
vs alternatives: unknown — repository lacks documentation comparing transport efficiency, feature completeness, or performance characteristics against other MCP server implementations
Provides a mechanism to register custom tools with the MCP server that become discoverable and callable by MCP clients. Tools are defined with schemas describing their inputs, outputs, and behavior, allowing clients to introspect available capabilities and invoke them with proper type validation and error handling through the MCP protocol.
Unique: unknown — insufficient documentation on tool schema format, validation mechanism, or how this implementation handles tool lifecycle compared to other MCP servers
vs alternatives: unknown — no comparative information available on tool registration complexity, schema expressiveness, or runtime performance
Enables the MCP server to expose resources (files, documents, data, or computed content) that clients can discover through resource listing endpoints and retrieve on-demand. Resources are identified by URIs and can include metadata, making them queryable and accessible to AI applications for context injection or processing.
Unique: unknown — insufficient information on resource indexing strategy, metadata schema, or how this server handles resource lifecycle and updates
vs alternatives: unknown — no documentation comparing resource discovery performance, content delivery efficiency, or feature parity with other MCP implementations
Implements the core MCP message handling layer that validates incoming JSON-RPC messages, routes them to appropriate handlers (tool calls, resource requests, capability queries), and ensures protocol compliance. The server enforces message schema validation and handles both synchronous request-response patterns and asynchronous notifications.
Unique: unknown — no documentation on validation implementation (schema validators used, custom logic), error handling strategy, or message routing architecture
vs alternatives: unknown — insufficient information to compare validation strictness, error reporting quality, or routing performance against reference implementations
Handles the MCP initialization handshake where the server advertises its capabilities (supported tools, resources, sampling endpoints) and negotiates feature support with connecting clients. This enables clients to discover what the server can do and adapt their behavior accordingly, supporting graceful degradation when certain features are unavailable.
Unique: unknown — insufficient documentation on capability schema, negotiation protocol, or how this server handles version mismatches
vs alternatives: unknown — no comparative information on feature discovery completeness or negotiation robustness
Provides standardized error response formatting following MCP protocol specifications, including error codes, messages, and optional error data. Catches exceptions from tool handlers and resource resolvers, converting them to structured JSON-RPC error responses. Enables clients to distinguish between different error types (invalid input, resource not found, handler exception) and respond appropriately.
Unique: Standardized error response formatting following MCP protocol enables clients to reliably distinguish error types and implement appropriate recovery logic without parsing error messages
vs alternatives: More structured than raw exception messages and more standardized than custom error formats, with built-in client compatibility
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 mcp_test at 24/100. mcp_test 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