| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Connects to PostgreSQL databases via native libpq protocol or TCP sockets to extract and expose complete schema metadata including tables, columns, indexes, constraints, and relationships. Uses information_schema queries to build a queryable representation of database structure without requiring ORM abstractions, enabling direct schema inspection for code generation or documentation purposes.
Unique: Implements MCP protocol binding for PostgreSQL schema access, allowing LLM agents to directly query database structure through standardized tool-calling interface rather than requiring custom REST APIs or database client libraries
vs alternatives: Provides schema introspection as an MCP tool callable by Claude, enabling AI agents to autonomously explore and reason about database structure without developer-written query wrappers
Connects to MySQL/MariaDB databases via TCP protocol to extract schema metadata including tables, columns, indexes, foreign keys, and constraints using INFORMATION_SCHEMA queries. Exposes database structure through MCP tool interface, enabling programmatic discovery of table relationships and column definitions without ORM dependencies.
Unique: Provides MySQL schema introspection as an MCP tool, allowing Claude and other LLM agents to autonomously query database structure through standardized tool-calling without custom API wrappers
vs alternatives: Simpler integration than building custom REST endpoints for schema discovery; leverages MCP protocol for direct agent access to MySQL metadata
Connects to Google Cloud Firestore using service account credentials to enumerate collections, sample documents, and infer document schema structure. Uses Firestore SDK to traverse collection hierarchies and analyze document fields, enabling runtime discovery of data structure without requiring pre-defined schemas or manual documentation.
Unique: Implements MCP tool binding for Firestore schema discovery, enabling LLM agents to explore NoSQL document structure through standardized interface without requiring custom Firebase client code
vs alternatives: Provides Firestore schema introspection as an MCP tool callable by Claude, allowing agents to autonomously discover collection and document structure without developer-written Firestore client wrappers
Manages connection lifecycle and routing across PostgreSQL, MySQL, and Firestore databases through a unified MCP tool interface. Handles credential storage, connection pooling, and request routing to appropriate database driver based on connection type, abstracting database-specific protocol details behind a common tool-calling surface.
Unique: Provides unified MCP tool interface for managing connections to heterogeneous databases (SQL and NoSQL), abstracting protocol differences and enabling single agent to query multiple database types
vs alternatives: Simpler than building separate MCP tools for each database type; unified routing layer reduces agent configuration complexity
Executes arbitrary SQL queries against PostgreSQL and MySQL databases through MCP tool interface, returning results as structured JSON with column metadata. Implements query result streaming for large result sets, handling pagination and memory-efficient result buffering to prevent agent context overflow.
Unique: Exposes SQL query execution as an MCP tool with result streaming, enabling LLM agents to execute dynamic queries while managing memory through pagination rather than loading entire result sets into context
vs alternatives: Safer than giving agents direct database access; MCP tool interface provides audit trail and allows for query validation/filtering before execution
Executes Firestore queries against collections using field-based filtering, ordering, and pagination through MCP tool interface. Translates filter conditions into Firestore SDK query API calls, returning documents as JSON with automatic type inference. Supports compound filters and ordering without requiring agents to understand Firestore query syntax.
Unique: Provides Firestore querying as an MCP tool with automatic filter translation, enabling agents to query NoSQL documents without understanding Firestore SDK syntax or composite index requirements
vs alternatives: Abstracts Firestore query complexity; agents can express queries in natural filter conditions rather than learning Firestore SDK API
Caches schema metadata from PostgreSQL, MySQL, and Firestore in memory with configurable TTL and manual invalidation triggers. Reduces repeated schema queries to databases, improving agent response latency for repeated schema introspection. Implements cache invalidation hooks for schema change detection or explicit refresh requests.
Unique: Implements configurable in-memory schema caching with TTL and manual invalidation, reducing repeated database queries for schema introspection in agent loops
vs alternatives: Faster than repeated schema queries for agents with frequent schema references; simpler than external cache systems but limited to single-process deployments
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 @iflow-mcp/db-mcp-tool at 21/100.
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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
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