| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements connection pooling for libSQL databases across three backend types: local file-based SQLite, local HTTP servers, and remote Turso cloud databases. Uses a pool manager pattern to maintain persistent connections with configurable pool sizes, reducing connection overhead for repeated queries. Automatically handles connection lifecycle management including idle timeout, reconnection on failure, and graceful shutdown.
Unique: Unified connection pooling abstraction across three distinct libSQL backends (file, HTTP, Turso) with automatic backend detection and configuration, eliminating the need for separate connection logic per backend type
vs alternatives: Simpler than managing raw libSQL connections or writing custom pooling logic, and more flexible than single-backend solutions by supporting local development and production Turso seamlessly
Executes SQL queries against pooled libSQL connections with full ACID transaction support including explicit BEGIN/COMMIT/ROLLBACK semantics. Implements transaction state tracking to prevent nested transaction errors and provides row-level result streaming for large result sets. Supports parameterized queries to prevent SQL injection while maintaining query performance through prepared statement caching.
Unique: Combines transaction state machine with parameterized query execution in a single abstraction, preventing common transaction nesting errors while maintaining SQL injection protection through automatic parameter binding
vs alternatives: More robust than raw SQL execution because it enforces transaction semantics and prevents injection attacks automatically, while remaining simpler than ORMs that add abstraction overhead
Queries libSQL system tables (sqlite_master, pragma statements) to extract comprehensive database schema metadata including table definitions, column types, indexes, constraints, and relationships. Returns structured metadata objects that describe the complete database structure without requiring external schema files or manual documentation. Caches schema metadata to reduce repeated system table queries.
Unique: Implements schema caching with manual invalidation control, allowing AI agents to avoid repeated system table queries while maintaining consistency guarantees through explicit refresh semantics
vs alternatives: More efficient than querying sqlite_master repeatedly because it caches results, and more complete than simple table listing because it extracts constraints, indexes, and relationships in a single operation
Creates full database backups by copying the entire database file (for file-based backends) or exporting via SQL dump (for HTTP/Turso backends). Supports incremental backup strategies by tracking modification timestamps and selective export of changed tables. Implements point-in-time recovery by maintaining backup metadata including timestamps and transaction IDs, enabling restoration to specific points in database history.
Unique: Implements unified backup interface across heterogeneous backends (file copy for local, SQL dump for HTTP/Turso) with point-in-time recovery metadata tracking, abstracting backend-specific backup mechanisms
vs alternatives: More comprehensive than simple file copying because it supports multiple backends and point-in-time recovery, while remaining simpler than enterprise backup solutions by focusing on database-specific operations
Implements cursor-based pagination for large result sets by maintaining server-side query state and returning configurable page sizes. Supports streaming results via iterator pattern to avoid loading entire datasets into memory, with automatic cursor management and position tracking. Enables efficient processing of million-row tables by yielding results in batches rather than materializing complete result sets.
Unique: Combines cursor-based pagination with streaming iterators to enable both stateful pagination (for web APIs) and stateless streaming (for pipelines) from the same underlying mechanism
vs alternatives: More memory-efficient than materializing full result sets, and more flexible than offset-based pagination because it handles concurrent modifications and large offsets without performance degradation
Manages database schema evolution through versioned migration files that track schema changes over time. Implements a migration state table to record which migrations have been applied, preventing duplicate execution and enabling rollback to previous schema versions. Supports both forward migrations (schema upgrades) and backward migrations (rollbacks) with automatic dependency resolution and conflict detection.
Unique: Implements bidirectional migration tracking with explicit rollback support and conflict detection, maintaining a complete audit trail of schema changes without requiring external migration tools
vs alternatives: Simpler than external migration tools like Flyway because it's built into the MCP server, while providing more control than ORM-based migrations by supporting raw SQL and explicit rollback definitions
Enforces row-level security policies by filtering query results based on user identity and permissions. Implements column-level masking to redact sensitive data (PII, credentials) from query results based on user roles. Uses a policy engine that evaluates security rules before returning data, preventing unauthorized access at the database layer rather than application layer.
Unique: Implements row-level security and column masking as first-class MCP capabilities, enforcing access control at the database layer before results are returned to clients, rather than relying on application-level filtering
vs alternatives: More secure than application-level filtering because it prevents data leakage through direct database access, while simpler than database-native RLS (PostgreSQL RLS) by using a centralized policy engine
Captures query execution metrics including execution time, rows scanned, and index usage patterns. Analyzes query performance against configurable thresholds to identify slow queries and missing indexes. Generates optimization suggestions based on execution plans and table statistics, such as recommending indexes on frequently filtered columns or suggesting query rewrites for inefficient joins.
Unique: Combines query execution monitoring with automated optimization suggestions in a single capability, analyzing execution plans and table statistics to generate actionable recommendations without requiring manual EXPLAIN analysis
vs alternatives: More proactive than manual query analysis because it continuously monitors performance and generates suggestions, while remaining simpler than enterprise APM tools by focusing specifically on database queries
+1 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.
Supabase scores higher at 42/100 vs libSQL by xexr at 25/100.
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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