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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes SELECT queries against Snowflake databases through the MCP protocol, streaming results back to the client with automatic connection pooling and query timeout management. The server implements a database client layer that handles Snowflake connector initialization, query parsing, and result serialization into structured JSON responses. Queries are validated before execution to ensure they contain only SELECT operations.
Unique: Implements read-only enforcement through SQL write detection (AST-level analysis of query strings) rather than database-level permissions, allowing the same Snowflake user account to be safely exposed to untrusted AI clients. The write detector analyzes query syntax patterns to block INSERT, UPDATE, DELETE, and CREATE operations before they reach the database.
vs alternatives: Safer than direct Snowflake JDBC/ODBC exposure because it enforces write restrictions at the application layer before queries reach the database, preventing accidental or malicious modifications even if the Snowflake user has write permissions.
Executes INSERT, UPDATE, DELETE, and CREATE TABLE operations against Snowflake when explicitly enabled via the --allow-write flag. The server implements a SQL write detector that parses query strings to identify write operations, then gates execution based on runtime configuration. Write operations are logged and tracked separately from read operations for audit purposes.
Unique: Implements opt-in write access through a server-level flag (--allow-write) combined with SQL write detection, creating a two-layer permission model. This allows operators to safely expose the same MCP server to different clients with different trust levels by controlling write access at deployment time rather than per-query.
vs alternatives: More flexible than database-level role restrictions because it allows the same Snowflake credentials to be used for both read-only and read-write scenarios depending on deployment configuration, without requiring separate database users or role management.
Provides tools to enumerate available databases, schemas within databases, and tables within schemas through a hierarchical traversal API. The server prefetches schema metadata at startup (if enabled) and caches it in memory, allowing fast schema exploration without repeated database round-trips. Each listing operation returns structured metadata including table names, column names, and data types.
Unique: Implements optional schema prefetching at server startup (controlled by --prefetch-schemas flag) that caches the entire database hierarchy in memory, enabling instant schema lookups without database round-trips. This is exposed as MCP resources (context://table/{table_name}) that Claude can reference directly in prompts.
vs alternatives: Faster than querying information_schema directly because it caches metadata in memory and exposes it as MCP resources, allowing Claude to reference table schemas in system prompts without executing queries. Reduces latency for schema-aware query generation from multiple database round-trips to zero.
Provides detailed column-level metadata for specific tables, including column names, data types, nullable constraints, and default values. The describe_table tool executes DESCRIBE TABLE queries against Snowflake and formats the results into a structured schema representation. This metadata is used by Claude to generate type-safe SQL queries and understand data semantics.
Unique: Exposes table schemas as MCP resources (context://table/{table_name}) that are automatically prefetched and cached at server startup, allowing Claude to reference full schema definitions in system prompts without executing queries. This enables schema-aware prompt engineering where the AI has immediate access to data structure information.
vs alternatives: More efficient than having Claude query information_schema because schema metadata is precomputed and exposed as MCP resources, reducing latency and token usage. Claude can reference table schemas directly in prompts rather than discovering them through query execution.
Provides an append_insight tool that allows Claude to accumulate observations and findings about data into a persistent memo resource (memo://insights). The memo is stored in memory during the session and can be referenced in subsequent queries and analysis. This creates a working memory for multi-step data exploration where Claude can record intermediate findings and build on them.
Unique: Implements session-scoped working memory through MCP resources, allowing Claude to maintain a persistent memo during a conversation without requiring external storage. The memo is exposed as a resource that Claude can reference in subsequent prompts, creating a form of in-session context accumulation.
vs alternatives: Simpler than external knowledge base systems because it requires no additional infrastructure — insights are stored in the MCP server's memory and automatically available to Claude. Enables multi-turn analysis workflows where Claude can build on previous findings without explicit context passing.
Implements a SQL write detector component that analyzes query strings to identify INSERT, UPDATE, DELETE, CREATE, ALTER, and DROP operations before they reach the database. The detector uses pattern matching on SQL keywords and syntax to classify queries as read or write operations. This enforcement layer prevents write operations when the server is running in read-only mode (default), even if the Snowflake user account has write permissions.
Unique: Implements write detection at the application layer using SQL keyword pattern matching rather than relying on database-level permissions, creating a defense-in-depth approach. The detector is configurable and can be bypassed only by explicit server-level flag (--allow-write), making read-only the secure default.
vs alternatives: More secure than database role-based access control because it prevents write operations before they reach the database, reducing the attack surface. Allows the same database credentials to be safely exposed to untrusted clients by enforcing write restrictions at the application layer.
Implements a complete MCP server that exposes Snowflake capabilities as tools (callable functions) and resources (data references) through the Model Context Protocol. The server handles MCP client connections, request routing, tool invocation, and resource serving. It implements the MCP specification for both stdio and HTTP transports, allowing integration with Claude Desktop and other MCP-compatible clients.
Unique: Implements the full MCP server specification including both tools (read_query, write_query, etc.) and resources (memo://insights, context://table/{table_name}), creating a bidirectional interface where Claude can both invoke operations and reference data. The server handles connection lifecycle, request routing, and error handling according to MCP standards.
vs alternatives: More standardized than custom REST APIs because it uses the Model Context Protocol, enabling seamless integration with Claude Desktop and other MCP clients without custom adapters. Exposes both tools and resources, allowing Claude to reference data in prompts and invoke operations, creating richer interactions than function-calling alone.
Manages Snowflake database connections through a connection pool that reuses connections across multiple queries, reducing connection overhead. The server loads Snowflake credentials from environment variables (SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, SNOWFLAKE_ACCOUNT, etc.) or command-line arguments, and initializes the Snowflake connector with these credentials. Connection parameters are validated at startup to fail fast if credentials are invalid.
Unique: Implements credential loading from environment variables with validation at server startup, following the 12-factor app pattern. Connection pooling is handled transparently by the snowflake-connector-python library, reducing per-query overhead while maintaining a simple API.
vs alternatives: More secure than hardcoding credentials because it loads them from environment variables, enabling deployment in containerized environments without embedding secrets in code. Connection pooling reduces latency compared to creating new connections per query.
+1 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 Snowflake 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