| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes SQLite database schema metadata (tables, columns, indexes, constraints) through the Model Context Protocol, allowing Claude and other MCP clients to programmatically discover database structure without manual SQL. Uses MCP's resource and tool abstractions to surface schema information as queryable endpoints that clients can call to understand table definitions, column types, and relationships before executing queries.
Unique: Implements schema introspection as native MCP tools/resources rather than requiring clients to parse raw SQLite metadata tables (sqlite_master), providing structured, client-friendly schema discovery that integrates seamlessly with Claude's tool-calling interface
vs alternatives: Simpler than generic SQLite clients because it's purpose-built for MCP integration and Claude's context window, exposing only relevant schema information through a standardized protocol rather than requiring clients to implement SQLite connection logic
Executes arbitrary SQL SELECT queries against a SQLite database and returns results in structured format, with support for parameterized queries to prevent SQL injection. The MCP server parses query parameters, validates syntax, and streams results back to the client through the MCP protocol, enabling Claude to run exploratory queries and retrieve data without direct database access.
Unique: Integrates parameterized query support directly into the MCP tool interface, allowing Claude to safely construct and execute queries with user-provided parameters without exposing raw SQL injection vectors, using SQLite's native parameter binding (? or :name placeholders)
vs alternatives: More secure than exposing raw SQL query tools because it enforces parameterization at the MCP layer and restricts to SELECT-only operations, reducing attack surface compared to generic database clients that allow all SQL operations
Lists all tables present in a SQLite database and provides metadata about each table (row count, creation context, associated indexes). Implemented as an MCP resource or tool that queries the sqlite_master system table and aggregates table statistics, enabling clients to understand database contents at a glance without manually inspecting schema.
Unique: Provides table enumeration as a first-class MCP tool rather than requiring clients to parse sqlite_master directly, with automatic row count aggregation and index association to give Claude immediate context about data volume and structure
vs alternatives: Faster discovery than manual schema inspection because it aggregates table statistics and index information in a single MCP call, reducing round-trips compared to clients that must query sqlite_master and then separately fetch index metadata
Retrieves detailed column metadata for a specified table, including column names, SQLite affinity types (TEXT, INTEGER, REAL, BLOB, NULL), nullability constraints, default values, and primary key information. Uses SQLite's PRAGMA table_info() to extract column definitions and correlates with PRAGMA table_list() for comprehensive type information, presenting results in a client-friendly format.
Unique: Exposes SQLite column metadata through a structured MCP tool that combines PRAGMA table_info() and table_list() results, providing type affinity information in a format that Claude can use for type-aware query construction and validation
vs alternatives: More precise than generic schema tools because it leverages SQLite's native PRAGMA commands to extract exact column definitions, enabling Claude to make type-aware decisions about query construction (e.g., avoiding type coercion in WHERE clauses)
Implements the Model Context Protocol server specification, registering database query and schema inspection capabilities as MCP tools that Claude and other compatible clients can discover and invoke. Uses MCP's tool definition schema to describe input parameters, output formats, and tool metadata, enabling seamless integration with Claude's tool-calling interface without requiring custom client code.
Unique: Implements full MCP server specification with proper tool registration, allowing Claude to discover and invoke database capabilities through the standardized MCP protocol rather than custom API endpoints, with support for both stdio and SSE transports
vs alternatives: More maintainable than custom API layers because it uses a standardized protocol that Claude natively understands, reducing integration code and enabling tool discovery without manual configuration
Provides domain-specific knowledge and optimizations for Apple Messages SQLite databases, including pre-configured table schemas, common query patterns, and message-specific metadata extraction. The server may include helper functions or pre-built queries for common Messages analysis tasks (e.g., conversation threads, message counts by contact, attachment analysis), tailored to the specific structure of Messages.app databases.
Unique: Specializes in Apple Messages.app database structure with pre-configured understanding of message tables, contact relationships, and conversation threading, enabling Claude to generate Messages-specific queries without requiring users to understand the underlying schema
vs alternatives: More useful than generic SQLite tools for Messages analysis because it includes domain knowledge about Messages.app table structure, relationships, and common analysis patterns, reducing the learning curve for users unfamiliar with the database schema
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 Messages SQLite Explorer at 30/100.
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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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