| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes arbitrary SQL queries against a PostgreSQL database through the Model Context Protocol (MCP) interface, translating LLM tool calls into native PostgreSQL client connections. Implements the MCP server specification to expose database operations as callable tools that Claude and other MCP-compatible clients can invoke, handling connection pooling, query parsing, and result serialization back to the LLM.
Unique: Implements the MCP server specification to expose PostgreSQL as a first-class tool for LLMs, rather than wrapping it in a REST API or custom protocol. Uses @modelcontextprotocol/sdk to handle MCP message serialization and tool registration, enabling direct integration with Claude and Cursor without middleware.
vs alternatives: Simpler than building custom REST APIs for database access and more standardized than direct JDBC/libpq bindings, as it leverages the emerging MCP ecosystem for LLM-database integration.
Registers database operations as callable MCP tools with JSON Schema definitions, allowing MCP clients to discover available database functions and their parameter requirements. Generates tool schemas that describe query execution, table introspection, and schema inspection capabilities, enabling Claude and other LLMs to understand what database operations are available and how to invoke them with proper parameters.
Unique: Automatically generates MCP tool schemas from PostgreSQL information_schema, enabling dynamic tool discovery without manual schema definition. Integrates with @modelcontextprotocol/sdk's tool registration API to expose database operations as first-class MCP tools.
vs alternatives: More discoverable than hardcoded API documentation and more flexible than static tool definitions, as schema changes are reflected in tool availability without code changes (after server restart).
Manages PostgreSQL client connections through a connection pool, handling connection initialization, reuse, and cleanup. Implements connection lifecycle hooks to ensure proper resource management, error recovery, and graceful shutdown. Abstracts away raw PostgreSQL client management, allowing the MCP server to handle multiple concurrent queries without exhausting database connections.
Unique: Uses the pg (node-postgres) library's built-in Pool class to manage connections, leveraging its event-driven architecture and automatic connection reuse. Integrates with MCP server lifecycle to ensure pools are properly initialized and drained on shutdown.
vs alternatives: More efficient than creating new connections per query and simpler than implementing custom connection management, as it relies on the mature pg library's pooling implementation.
Converts PostgreSQL query results (rows, metadata, types) into JSON-serializable format suitable for MCP protocol transmission. Handles type conversion for PostgreSQL-specific types (arrays, JSON, UUIDs, timestamps) into JSON-compatible representations, and includes query metadata (row count, execution time) in the response. Ensures that complex database types are properly represented in the LLM context.
Unique: Leverages the pg library's built-in type parsing to handle PostgreSQL-specific types, then applies custom serialization logic to convert them to JSON. Preserves type information in the response so the LLM understands the semantic meaning of each field.
vs alternatives: More complete than simple JSON.stringify() and more maintainable than custom type handlers, as it builds on pg's type system which is updated with PostgreSQL versions.
Implements the MCP server specification to handle incoming tool call requests, route them to appropriate database operations, and return results in MCP-compliant format. Manages the request-response cycle including error handling, timeout management, and protocol-level validation. Translates between MCP's JSON-RPC-like message format and internal database operation calls.
Unique: Implements the MCP server interface from @modelcontextprotocol/sdk, handling the protocol-level message routing and validation. Uses the SDK's built-in tool registration and invocation mechanisms rather than implementing MCP protocol parsing from scratch.
vs alternatives: More maintainable than custom MCP protocol implementation and automatically compatible with future MCP protocol versions, as it relies on the official SDK which is updated by Anthropic.
Enables Cursor IDE to access PostgreSQL databases through the MCP protocol, allowing developers to use Claude within Cursor to generate and execute SQL queries directly against their database. Registers the MCP server as a Cursor tool provider, making database operations available in the Cursor chat interface and code generation workflows. Facilitates seamless integration between Cursor's AI features and live database access.
Unique: Specifically targets Cursor IDE's MCP integration, enabling database access directly within the IDE's AI chat and code generation workflows. Leverages Cursor's native MCP support to avoid requiring custom plugins or extensions.
vs alternatives: More integrated than external database tools and more convenient than switching between Cursor and separate database clients, as it keeps database operations within the IDE's AI interface.
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 user-postgresql-mcp at 22/100. user-postgresql-mcp leads on ecosystem, while Supabase is stronger on quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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