| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes PostgreSQL operations as MCP Tools through a standardized JSON-RPC 2.0 transport layer, enabling LLM clients to invoke database queries and schema operations as first-class capabilities. The server implements the MCP protocol primitives (Tools, Resources, Prompts) to bridge SQL execution with LLM reasoning, using the official MCP SDK to handle bidirectional communication and request/response serialization.
Unique: Official MCP server implementation using the canonical MCP SDK, ensuring protocol compliance and alignment with MCP steering group standards. Implements the full MCP tool registration pattern with schema-based function discovery, unlike ad-hoc REST API wrappers.
vs alternatives: Provides native MCP protocol support out-of-the-box, eliminating the need for custom JSON-RPC adapters or REST-to-MCP bridges that third-party PostgreSQL tools would require.
Executes SQL SELECT queries against PostgreSQL with enforced read-only constraints, preventing accidental or malicious write operations (INSERT, UPDATE, DELETE, DROP). The server validates query syntax and intent before execution, using connection pooling to manage database resources efficiently and returning results as structured JSON with row-level metadata.
Unique: Implements read-only enforcement at the MCP server layer (not relying on database role restrictions), with explicit query validation before execution. This ensures safety even if the PostgreSQL user account has broader permissions.
vs alternatives: Safer than direct database connections or REST APIs that expose write operations; more flexible than database-level read-only roles because it can be toggled per MCP server instance without modifying PostgreSQL permissions.
Logs all query executions, errors, and connection events to stdout or structured log files, enabling debugging and monitoring of MCP server behavior. The server records query text, execution time, result row count, and error details with timestamps. Supports structured logging (JSON format) for integration with log aggregation systems like ELK or Datadog.
Unique: Integrates logging at the MCP server layer, capturing both MCP protocol events and PostgreSQL query execution, providing end-to-end visibility into LLM-to-database interactions.
vs alternatives: More comprehensive than PostgreSQL query logs alone because it captures MCP-level context (client identity, request timing); more actionable than generic application logs because it includes database-specific metrics.
Inspects PostgreSQL schema to expose table structures, column definitions, indexes, and relationships as queryable metadata. The server implements schema discovery tools that retrieve information_schema data and format it as structured JSON, enabling LLMs to understand database structure before generating queries. Supports filtering by schema, table, or column patterns.
Unique: Exposes schema metadata as MCP Resources (not just Tools), allowing clients to cache and reference schema information across multiple queries. This reduces redundant metadata queries and enables context-aware prompt engineering.
vs alternatives: More efficient than ad-hoc DESCRIBE or SHOW TABLES queries because schema metadata is pre-fetched and formatted consistently; integrates with MCP's resource caching layer for better performance.
Manages PostgreSQL connections using a connection pool (default 10 connections) to reuse database connections across multiple queries, reducing connection overhead and improving throughput. The server configures pool parameters (min/max connections, idle timeout, connection timeout) and handles connection lifecycle (acquire, release, error recovery) transparently. Implements connection validation and automatic reconnection on failure.
Unique: Integrates connection pooling at the MCP server layer, not delegating to application code. This ensures all MCP Tool invocations benefit from pooling without requiring client-side configuration.
vs alternatives: More efficient than creating new connections per query (which adds 100-500ms overhead); simpler than requiring clients to manage their own connection pools.
Supports parameterized SQL queries using PostgreSQL prepared statements, separating query structure from data values to prevent SQL injection attacks. The server accepts query templates with placeholder parameters and binds user-supplied values safely using the pg library's parameterization mechanism. Parameters are type-checked and escaped by the PostgreSQL driver before execution.
Unique: Enforces parameterized queries at the MCP server layer, preventing LLM clients from accidentally constructing vulnerable queries through string interpolation. The server validates parameter count and types before execution.
vs alternatives: More secure than string-based query construction; provides the same SQL injection protection as ORMs but with the flexibility of raw SQL.
Executes complex SQL queries including JOINs, GROUP BY, aggregations (SUM, COUNT, AVG, MAX, MIN), subqueries, and window functions. The server parses and validates query structure to ensure read-only compliance while allowing sophisticated analytical queries. Results are returned as nested JSON structures that preserve column aliases and aggregation results.
Unique: Supports the full PostgreSQL query language (except mutations) without query rewriting or simplification, allowing LLMs to leverage advanced SQL features like window functions and CTEs directly.
vs alternatives: More powerful than simplified query builders that restrict to single-table queries; more flexible than pre-defined analytical endpoints because it supports arbitrary query composition.
Validates SQL syntax and execution errors, returning detailed error messages that help LLM clients understand and correct query failures. The server catches PostgreSQL errors (syntax errors, constraint violations, type mismatches) and formats them as structured JSON responses with error codes, messages, and context. Distinguishes between client errors (invalid SQL) and server errors (connection failures) for appropriate retry logic.
Unique: Formats PostgreSQL errors as MCP-compatible JSON responses with structured error codes and context, enabling LLM clients to parse and respond to errors programmatically rather than parsing error strings.
vs alternatives: More informative than generic 'query failed' responses; safer than exposing raw PostgreSQL error messages because the server can sanitize sensitive information.
+3 more capabilities
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.
PostgreSQL MCP Server scores higher at 61/100 vs Supabase at 42/100.
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