| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes SQL queries against 8+ database backends (PostgreSQL, MariaDB, BigQuery, MS SQL Server, Redshift, MySQL, SQLite, Oracle) through ConnectorX's Rust-based connector abstraction layer. ConnectorX handles connection pooling, query compilation, and result streaming without materializing full result sets in memory, enabling efficient execution of large queries. The MCP tool wraps ConnectorX's query API to expose database execution as a standardized Model Context Protocol resource.
Unique: Uses ConnectorX's Rust-based columnar data loading architecture to stream results directly to CSV/Parquet without intermediate Python object materialization, avoiding memory overhead that traditional JDBC/psycopg2 drivers incur. Exposes this as an MCP tool, enabling LLM agents to execute SQL across 8+ database backends through a unified interface.
vs alternatives: More memory-efficient than LangChain's SQLDatabase tool (which materializes results in Python) and supports more database backends than most MCP SQL tools; ConnectorX's Rust implementation provides 2-10x faster data transfer than pure Python drivers for large result sets.
Streams SQL query results directly to CSV or Parquet files without buffering the full result set in memory. Uses ConnectorX's columnar data model to write results in batches, enabling efficient export of multi-gigabyte datasets. The streaming approach prevents out-of-memory errors on large queries and allows results to be consumed incrementally by downstream tools or LLM context windows.
Unique: Leverages ConnectorX's native columnar data representation to write results directly to Parquet/CSV without intermediate Python object conversion, avoiding the memory and CPU overhead of pandas DataFrame materialization. Streaming batches enable processing of result sets larger than available RAM.
vs alternatives: More efficient than pandas-based export (which materializes entire DataFrame in memory) and faster than traditional database drivers that serialize to Python objects; Parquet output preserves schema and enables zero-copy reads in downstream tools like DuckDB.
Wraps the SQL execution and result export functionality as an MCP (Model Context Protocol) tool named 'run_sql', exposing database queries as a standardized resource that Claude, Cline, and other MCP-compatible clients can invoke. The MCP server handles request/response serialization, error handling, and result streaming through the MCP transport layer, abstracting database connection management from the client.
Unique: Implements MCP server pattern to expose ConnectorX database execution as a first-class tool in the Model Context Protocol ecosystem, enabling LLM agents to query databases with the same interface they use for file systems, APIs, and other resources. Handles connection lifecycle and result streaming within the MCP protocol layer.
vs alternatives: More standardized than custom LangChain tools (uses MCP instead of proprietary integration) and more flexible than direct database drivers (supports multiple clients and tools); MCP abstraction enables the same database tool to work with Claude, Cline, and future MCP-compatible AI systems.
Executes SQL queries with parameter binding to prevent SQL injection attacks. The implementation accepts query strings with placeholders (e.g., '?' or ':param') and separate parameter values, passing both to ConnectorX's query execution layer which handles safe parameter substitution at the database driver level. This prevents untrusted input (from LLM outputs or user input) from being interpreted as SQL code.
Unique: Delegates parameter binding to ConnectorX's database driver layer rather than implementing custom escaping, ensuring that parameter substitution follows each database's native protocol (e.g., PostgreSQL wire protocol, MySQL binary protocol). This prevents both first-order SQL injection and database-specific injection variants.
vs alternatives: More secure than string-based query construction (which LLMs often generate) and more robust than regex-based SQL sanitization; leverages database driver's native parameter handling, which is battle-tested and handles edge cases (e.g., binary data, special characters) correctly.
Manages database connections through ConnectorX's connection pooling layer, which reuses connections across multiple queries to reduce connection overhead. The MCP server maintains connection state and handles connection lifecycle (creation, reuse, cleanup) transparently. Pooling is configured implicitly based on ConnectorX defaults, with connection timeouts and retry logic handled by the underlying database driver.
Unique: Leverages ConnectorX's built-in connection pooling (implemented in Rust for low overhead) rather than implementing custom pooling in Python, reducing per-query connection overhead to microseconds. Pool state is managed transparently by ConnectorX, requiring no explicit configuration from the MCP server.
vs alternatives: More efficient than creating new connections per query (which adds 100-500ms latency per query) and simpler than managing custom connection pools in Python; ConnectorX's Rust implementation provides lower memory overhead than SQLAlchemy's pooling.
Captures database errors (connection failures, syntax errors, permission errors, timeouts) from ConnectorX and translates them into MCP error responses with human-readable messages. The implementation preserves database-specific error codes and context while sanitizing sensitive information (e.g., internal server details). Errors are returned to the MCP client with appropriate HTTP-like status codes and error descriptions.
Unique: Translates ConnectorX's Rust-level error types (which vary by database backend) into a unified MCP error response format, enabling consistent error handling across heterogeneous databases. Preserves database-specific error codes for debugging while sanitizing sensitive details.
vs alternatives: More informative than generic 'query failed' errors and more consistent than passing raw database errors to LLMs; error translation enables agents to distinguish between retryable (timeout) and non-retryable (syntax) failures.
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 run-sql-connectorx at 24/100.
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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