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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes SQL queries against 8+ database systems (PostgreSQL, MySQL, SQL Server, BigQuery, Oracle, SQLite, Redshift, CockroachDB) through a single MCP tool interface. Routes queries through the Legion Query Runner abstraction layer, which handles database-specific connection management, SQL dialect normalization, result set formatting, and connection pooling. The FastMCP server maintains a DbContext state manager that tracks active database connections and query history across multiple database instances.
Unique: Uses Legion Query Runner abstraction to provide consistent query execution across 8 database systems with different SQL dialects and connection models, routing through FastMCP's DbContext state manager rather than requiring separate client libraries per database type
vs alternatives: Unified MCP interface eliminates need for database-specific client management in AI agents, whereas alternatives like direct JDBC/psycopg2 require separate connection handling per database type
Automatically discovers database schemas (tables, columns, constraints, indexes) and exposes them as MCP Resources in a standardized JSON hierarchical format. The system introspects the connected database on initialization, generates schema metadata, and makes this information available to AI clients without requiring manual schema definition. Supports schema discovery across all 8 supported database types with database-specific introspection queries.
Unique: Exposes discovered schemas as MCP Resources (not just Tools), enabling AI clients to access schema context directly in their context window rather than requiring schema queries through tool calls, reducing latency for schema-aware reasoning
vs alternatives: Automatic schema discovery via MCP Resources eliminates manual schema documentation and separate schema query tools, whereas alternatives like Prisma or SQLAlchemy require explicit schema definition or separate introspection queries
Provides native support for PostgreSQL-compatible databases (Redshift, CockroachDB) by leveraging PostgreSQL drivers and SQL dialect compatibility. These systems are treated as PostgreSQL variants in the Legion Query Runner, using the same connection management and query execution paths as native PostgreSQL while handling system-specific quirks (e.g., Redshift's distributed query optimization, CockroachDB's distributed transaction semantics).
Unique: Treats Redshift and CockroachDB as PostgreSQL variants in Legion Query Runner, enabling single-driver support for multiple distributed SQL systems rather than requiring separate drivers or connection management
vs alternatives: PostgreSQL driver compatibility eliminates need for separate Redshift or CockroachDB drivers, whereas alternatives like native Redshift clients require system-specific connection handling
Provides native support for cloud and enterprise databases (BigQuery, Oracle) through specialized drivers and API integrations. BigQuery uses the google-cloud-bigquery SDK for cloud API integration, while Oracle uses cx_Oracle for enterprise database access. Each system has database-specific connection management, authentication handling, and result formatting through the Legion Query Runner abstraction.
Unique: Integrates cloud (BigQuery) and enterprise (Oracle) databases through specialized drivers in Legion Query Runner, handling cloud-specific authentication and API requirements transparently
vs alternatives: Unified interface for cloud and enterprise databases eliminates need for separate BigQuery and Oracle client libraries, whereas alternatives require separate SDKs and authentication handling per system
Supports configuration of single or multiple databases through three independent configuration sources: environment variables (DB_TYPE/DB_CONFIG or DB_CONFIGS), command-line arguments (--db-type/--db-config or --db-configs), and MCP settings JSON. The system automatically processes configurations, generates unique database IDs, initializes Legion Query Runners for each database, and maintains runtime state including query history. Configuration precedence follows: MCP settings > CLI arguments > environment variables.
Unique: Supports three independent configuration sources with explicit precedence rules and automatic DbConfig object generation, enabling both single-database and multi-database setups without code changes, whereas alternatives like SQLAlchemy require programmatic configuration
vs alternatives: Configuration flexibility across environment variables, CLI, and MCP settings eliminates need for separate configuration files or code changes per deployment, whereas tools like psycopg2 or mysql-connector require hardcoded connection strings or separate config files
Manages connection pooling, lifecycle, and error recovery for each database system through the Legion Query Runner abstraction. Handles database-specific connection management (native drivers for PostgreSQL/MySQL/SQL Server, cloud API integration for BigQuery, file-based connections for SQLite) with automatic connection validation, timeout handling, and graceful degradation. The DbContext state manager tracks active connections and maintains query history across the server lifetime.
Unique: Abstracts connection pooling across 8 database systems with different connection models (native drivers, cloud APIs, file-based) through a unified Legion Query Runner interface, eliminating need for database-specific pool configuration
vs alternatives: Unified connection pooling abstraction handles database-specific lifecycle management transparently, whereas alternatives like SQLAlchemy require explicit pool configuration per database engine and manual connection lifecycle management
Exposes database operations as MCP Tools with standardized input schemas and output formats. Each tool accepts database identifiers, SQL queries, and optional parameters, returning structured results with execution metadata. The FastMCP server registers tools dynamically based on configured databases, enabling AI clients to discover and invoke database operations through the MCP protocol's tool-calling mechanism.
Unique: Registers database operations as MCP Tools with dynamic schema generation based on configured databases, enabling tool discovery and type-safe invocation through the MCP protocol rather than requiring custom tool implementations
vs alternatives: MCP tool interface provides standardized tool discovery and invocation for AI clients, whereas alternatives like direct API calls or custom function calling require separate tool definition and registration per application
Normalizes SQL queries across different database systems by handling dialect-specific syntax differences. The Legion Query Runner translates queries for database-specific requirements (e.g., BigQuery's LIMIT vs SQL Server's TOP, PostgreSQL's RETURNING vs MySQL's LAST_INSERT_ID), manages result set formatting, and handles error translation. Supports parameterized queries to prevent SQL injection while maintaining dialect compatibility.
Unique: Abstracts SQL dialect differences across 8 database systems through Legion Query Runner, enabling consistent query semantics while handling database-specific syntax and result formatting automatically
vs alternatives: Unified dialect abstraction eliminates need for database-specific query variants, whereas alternatives like SQLAlchemy ORM require explicit dialect handling or separate query definitions per database
+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.
Supabase scores higher at 42/100 vs Database at 27/100.
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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