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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Establishes connections to SQL (PostgreSQL, MySQL, SQLite), NoSQL (MongoDB, Redis), Graph (Neo4j), and object databases through a unified connection abstraction layer. The CLI parses connection strings, manages authentication credentials, and automatically introspects database schemas to build an in-memory representation of tables, collections, indexes, and relationships without requiring manual schema definition.
Unique: Unified abstraction layer supporting 5+ database paradigms (SQL, NoSQL, Graph, Cache, Object) through a single CLI interface with automatic schema discovery, rather than separate tools per database type
vs alternatives: Faster than DBeaver or DataGrip for quick schema exploration because it's lightweight CLI-first with no GUI overhead, and covers more database types than database-specific tools like mongo-shell or psql
Accepts natural language questions about data and converts them to database-specific query syntax (SQL, MongoDB query language, Cypher, etc.) using an LLM backend. The system provides the LLM with the introspected schema context, executes the generated query against the connected database, and returns results with optional explanation of the query logic. Supports multi-turn conversation to refine queries iteratively.
Unique: Injects live schema introspection into LLM context for each query, enabling accurate generation across heterogeneous database types, rather than using static prompt templates or fine-tuned models
vs alternatives: More flexible than database-specific AI tools (e.g., SQL.ai) because it works across SQL, NoSQL, and Graph databases with the same interface, and provides schema context dynamically rather than requiring manual schema uploads
Supports writing shell scripts or CLI commands that execute templated queries with variable substitution, conditional logic, and output formatting. Enables automation of repetitive database tasks (backups, data exports, cleanup jobs) without writing application code. Integrates with standard Unix pipes and redirection for composability with other tools.
Unique: Native CLI integration with Unix pipes and shell scripting, enabling database automation without application frameworks or external dependencies
vs alternatives: Lighter-weight than Python scripts or Airflow DAGs for simple automation tasks, and more portable because it uses standard shell syntax
Displays query results in a paginated, interactive TUI (terminal user interface) with column sorting, row filtering, and data type-aware formatting. Supports exporting results to CSV, JSON, or other formats. Implements keyboard navigation and search across result sets without requiring additional tools or context switching.
Unique: Native TUI implementation with database-aware formatting (dates, JSON, binary data) rather than generic table rendering, enabling immediate exploration without external viewers
vs alternatives: Faster than exporting to CSV and opening in Excel for quick exploration, and more intuitive than piping to less or awk for developers unfamiliar with Unix text tools
Translates queries between database-specific syntaxes or executes queries written in a normalized intermediate format across different database types. For example, a single query structure can be executed against PostgreSQL, MongoDB, and Neo4j with automatic syntax adaptation. Uses a query abstraction layer that maps common operations (filter, project, join, aggregate) to database-native implementations.
Unique: Implements a query abstraction layer that maps to SQL, MongoDB query language, Cypher, and Redis commands simultaneously, rather than requiring separate query builders per database type
vs alternatives: More comprehensive than ORM-based solutions (Sequelize, Mongoose) because it covers non-relational databases and graph databases, and faster than manual query rewriting for multi-database exploration
Stores and manages database connection profiles (credentials, connection strings, authentication methods) in a local encrypted or plaintext configuration file. Supports quick switching between saved connections via CLI flags or interactive selection. Implements credential management patterns to avoid hardcoding secrets in command history or shell scripts.
Unique: Unified profile management across 5+ database types with a single configuration format, rather than separate credential stores per database tool
vs alternatives: More convenient than environment variables for managing multiple connections, and more secure than hardcoding credentials in shell scripts or config files
Watches connected databases for schema changes, new tables/collections, or data modifications and alerts the user via CLI notifications or logs. Implements polling or event-based monitoring depending on database capabilities (e.g., PostgreSQL LISTEN/NOTIFY, MongoDB change streams, Redis keyspace notifications). Tracks changes over time with optional historical logging.
Unique: Unified monitoring interface across SQL, NoSQL, and Graph databases using database-native change detection mechanisms (LISTEN/NOTIFY, change streams, polling) rather than external CDC tools
vs alternatives: Lighter-weight than Debezium or other CDC platforms for simple monitoring use cases, and integrated into the same CLI rather than requiring separate infrastructure
Imports data from CSV, JSON, Parquet, or other formats into connected databases with automatic type inference and schema mapping. Supports batch inserts, upserts, and conflict resolution strategies. Implements streaming for large files to avoid memory exhaustion and provides progress tracking and error reporting for failed records.
Unique: Supports bulk loading across heterogeneous databases (SQL, NoSQL, Graph) with a single command and automatic schema adaptation, rather than database-specific import tools
vs alternatives: Faster than manual INSERT statements or ORM bulk operations for large datasets, and more flexible than database-native COPY/LOAD commands because it works across multiple database types
+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.
Supabase scores higher at 42/100 vs WhoDB at 22/100.
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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