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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 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 enables Claude and other MCP-compatible clients to discover and invoke server capabilities. Uses the MCP transport layer to handle bidirectional communication between client and server, managing request/response lifecycle and resource initialization handshakes.
Unique: unknown — insufficient data on specific implementation details, tool registry patterns, or transport layer choices
vs alternatives: Provides standardized MCP protocol compliance enabling interoperability with Claude and future MCP clients, versus custom REST APIs that require individual integration work per client
Defines and validates tool schemas using JSON Schema or similar type systems, allowing MCP clients to understand tool signatures, required/optional parameters, return types, and constraints before invocation. Implements schema introspection so clients can dynamically discover available tools and their capabilities without hardcoded knowledge of the server's API surface.
Unique: unknown — insufficient data on schema format choices, validation strictness, or support for advanced schema patterns
vs alternatives: Enables AI clients to understand and validate tool invocations declaratively via schemas, versus imperative approaches requiring clients to hardcode tool knowledge or rely on natural language descriptions
Exposes server-side resources (files, documents, API responses, database records) through MCP resource endpoints using URI-based addressing. Clients can request resources by URI, and the server returns content with optional MIME type metadata, enabling Claude to access and reason over server-managed content without direct file system or database access.
Unique: unknown — insufficient data on resource caching strategies, access control implementation, or support for streaming large resources
vs alternatives: Provides URI-based resource access with server-side filtering and access control, versus embedding all content in tool parameters or requiring clients to manage direct database/file connections
Manages bidirectional JSON-RPC 2.0 communication between MCP client and server, implementing request ID correlation to match responses with requests, handling timeouts, and managing connection state. Supports both client-initiated requests (tool calls) and server-initiated notifications (async events), enabling full-duplex interaction patterns.
Unique: unknown — insufficient data on request queuing strategy, timeout implementation, or handling of connection failures
vs alternatives: Implements full JSON-RPC 2.0 spec with request correlation, versus simpler request/response patterns that cannot handle concurrent operations or server-initiated events
Abstracts the underlying transport mechanism (stdio, HTTP, WebSocket, etc.) behind a unified MCP interface, allowing the same server implementation to work across different connection types. Handles transport-specific concerns like framing, serialization, and connection lifecycle management while exposing a consistent message-passing API to the server logic.
Unique: unknown — insufficient data on specific transport implementations supported, abstraction layer design, or performance characteristics per transport
vs alternatives: Provides unified transport abstraction enabling single codebase to work across stdio, HTTP, and WebSocket, versus transport-specific implementations requiring separate code paths for each connection type
Provides pluggable transport implementations (stdio, SSE, WebSocket) that abstract the underlying communication protocol while maintaining MCP message semantics. The server can operate over different transports without changing tool or resource logic, enabling deployment flexibility (local CLI, HTTP server, WebSocket agent). Each transport handles serialization, framing, and error handling independently.
Unique: unknown — insufficient data on transport implementation (e.g., whether it uses adapter pattern, middleware, or specific library choices)
vs alternatives: Decouples MCP logic from transport details, enabling single server implementation to work across stdio, HTTP, and WebSocket without duplication
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 bk_mcp at 24/100. bk_mcp 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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