| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes SQLite database operations as MCP tools through a standardized JSON-RPC 2.0 transport layer, enabling LLM clients to discover and invoke database capabilities via the MCP protocol primitives. The server implements the MCP Tools interface, registering discrete database operations (query execution, schema inspection, table creation) as callable tools with JSON Schema definitions for input validation and type safety.
Unique: Official reference implementation of MCP Tools interface for SQLite, demonstrating the standardized pattern for exposing database capabilities through MCP's JSON Schema-based tool registry rather than custom API frameworks
vs alternatives: Provides protocol-native database access for MCP clients without requiring REST API scaffolding, enabling direct LLM tool calling with built-in schema validation
Executes arbitrary SQL queries against a SQLite database file and returns results as structured JSON objects, with built-in error handling for malformed SQL, constraint violations, and database locking. The implementation parses query results into row-based JSON format, enabling LLMs to reason over tabular data natively. Supports both SELECT queries (returning result sets) and DML operations (INSERT, UPDATE, DELETE) with transaction semantics.
Unique: Exposes raw SQL execution as an MCP tool with automatic result serialization to JSON, allowing LLMs to construct and execute queries directly without intermediate query builders or ORMs
vs alternatives: Simpler and more flexible than ORM-based approaches because it gives LLMs full SQL expressiveness while maintaining error isolation through MCP's structured error responses
Executes SELECT queries with JOIN clauses (INNER, LEFT, RIGHT, FULL OUTER) across multiple tables, returning flattened result sets with columns from all joined tables. The server handles SQLite's join semantics, including NULL propagation in outer joins and duplicate row handling. This enables LLM agents to correlate data across tables without understanding join syntax, by specifying tables and join conditions as parameters.
Unique: Executes join queries through the same MCP tool interface as single-table queries, with no special handling required. The server relies on SQLite's native join engine, ensuring correct NULL handling and join semantics according to SQL standards.
vs alternatives: More flexible than denormalized data structures because it supports arbitrary join conditions; more efficient than client-side joins because it leverages SQLite's optimized join engine.
Provides MCP tools to create indexes on table columns and retrieve query execution plans (EXPLAIN QUERY PLAN output) to help optimize slow queries. The server accepts index definitions (table, columns, uniqueness) and generates CREATE INDEX statements, then validates that indexes are created successfully. For query optimization, the server executes EXPLAIN QUERY PLAN and returns the execution plan in a structured format, allowing LLM agents to understand query performance and suggest index creation.
Unique: Exposes both index creation and query plan analysis through MCP tools, enabling LLM agents to close the feedback loop: analyze slow queries with EXPLAIN, create indexes, and re-analyze to verify improvements. The server returns EXPLAIN output in a structured format suitable for LLM analysis.
vs alternatives: More actionable than raw EXPLAIN output because it's formatted for LLM consumption; more flexible than automatic indexing because it allows agents to reason about index trade-offs (storage vs. query speed).
Provides tools to inspect SQLite database schema including table definitions, column types, constraints, indexes, and relationships. The implementation queries SQLite's built-in metadata tables (sqlite_master, PRAGMA table_info, PRAGMA foreign_key_list) and returns structured schema information as JSON, enabling LLMs to understand database structure before constructing queries. Supports discovery of all tables, views, and their column definitions with type information.
Unique: Exposes SQLite's PRAGMA-based metadata system as an MCP tool, allowing LLMs to query schema information programmatically rather than relying on documentation or manual inspection
vs alternatives: More comprehensive than simple table listing because it includes column types, constraints, and relationships — giving LLMs the full context needed to construct type-safe queries
Allows LLMs to create new tables in the SQLite database by executing CREATE TABLE statements with full DDL support including column definitions, constraints (PRIMARY KEY, UNIQUE, NOT NULL, CHECK), and indexes. The implementation validates DDL syntax and enforces schema constraints before execution, preventing invalid table definitions. Supports both simple table creation and complex schemas with foreign keys and composite keys.
Unique: Exposes SQLite DDL as an MCP tool, enabling LLMs to define schemas programmatically with full constraint support rather than being limited to predefined tables
vs alternatives: More flexible than schema-as-code frameworks because it allows LLMs to generate schemas dynamically based on runtime requirements, though with less validation than dedicated migration tools
Supports SQLite transactions (BEGIN, COMMIT, ROLLBACK) to ensure ACID properties for multi-step data operations. The implementation manages transaction state and allows LLMs to group multiple SQL operations into atomic units, rolling back all changes if any operation fails. Enables data consistency guarantees for complex workflows like data imports or multi-table updates.
Unique: Exposes SQLite transaction control as MCP tools, allowing LLMs to reason about and manage transaction boundaries explicitly rather than relying on auto-commit behavior
vs alternatives: Provides stronger consistency guarantees than stateless query execution because LLMs can group operations into atomic units, though requires careful session management in the MCP client
Enables LLMs to execute analytical SQL queries including GROUP BY, aggregation functions (COUNT, SUM, AVG, MIN, MAX), JOINs, and window functions to perform data analysis directly on SQLite. The implementation returns aggregated results as JSON, allowing LLMs to derive insights from structured data without exporting to external tools. Supports complex queries with subqueries and CTEs (Common Table Expressions).
Unique: Exposes full SQL analytical capabilities (GROUP BY, window functions, CTEs) as MCP tools, enabling LLMs to perform sophisticated data analysis without external BI tools or data export
vs alternatives: More powerful than simple row retrieval because it allows LLMs to compute aggregates and identify patterns directly in the database, reducing data transfer and enabling iterative analysis
+4 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.
SQLite MCP Server scores higher at 61/100 vs Supabase at 42/100.
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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