| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Establishes and manages bidirectional communication channels with MCP servers using the Model Context Protocol specification. Handles transport layer abstraction (stdio, SSE, WebSocket) and maintains connection state, allowing clients to discover and invoke remote capabilities exposed by MCP servers without direct knowledge of their implementation details.
Unique: unknown — insufficient data on whether this uses native MCP transport abstraction vs custom wrapper, or specific connection pooling strategies
vs alternatives: Provides standardized MCP client for Maz-UI ecosystem; positioning vs alternatives depends on transport efficiency and reconnection resilience which are not documented
Queries connected MCP servers to enumerate available tools, resources, and prompts with their full JSON schemas, parameter definitions, and usage documentation. Implements the MCP resource discovery protocol to build a local registry of remote capabilities that can be dynamically invoked without hardcoding tool definitions.
Unique: unknown — insufficient data on caching strategy, schema normalization approach, or how it handles schema versioning and compatibility
vs alternatives: Provides standardized schema discovery aligned with MCP spec; differentiation depends on caching efficiency and schema transformation capabilities which are undocumented
Executes tools on connected MCP servers by marshaling parameters according to their JSON schemas, sending requests over the MCP protocol, and unmarshaling responses back into typed objects. Handles parameter validation, type coercion, and error propagation from remote tool execution failures.
Unique: unknown — insufficient data on parameter validation strictness, error handling patterns, or support for streaming/async tool responses
vs alternatives: Provides MCP-compliant tool invocation; differentiation depends on validation rigor and error recovery mechanisms which are not documented
Retrieves content from resources exposed by MCP servers using URI-based addressing and MIME type negotiation. Implements the MCP resource protocol to fetch text, binary, or structured data from remote sources without requiring direct file system or API access, enabling LLM agents to read files, fetch web content, or access databases through a unified interface.
Unique: unknown — insufficient data on caching strategy, streaming support, or content transformation capabilities
vs alternatives: Provides MCP-standard resource access; differentiation depends on caching efficiency and support for large/streaming resources which are undocumented
Retrieves prompt templates from MCP servers and renders them with injected context variables, enabling LLM agents to use server-defined prompts with dynamic parameter substitution. Implements the MCP prompts protocol to fetch prompt definitions, validate parameters against schemas, and produce final prompt text ready for LLM consumption.
Unique: unknown — insufficient data on template syntax, parameter substitution approach, or support for conditional/computed parameters
vs alternatives: Provides MCP-compliant prompt retrieval and rendering; differentiation depends on template expressiveness and caching which are not documented
Subscribes to and processes notifications/events emitted by MCP servers, enabling real-time updates about resource changes, tool execution results, or server state changes. Implements the MCP notifications protocol with event filtering and handler registration to support reactive agent patterns where agents respond to server-side events.
Unique: unknown — insufficient data on event ordering guarantees, filtering capabilities, or persistence/replay mechanisms
vs alternatives: Provides MCP-standard event subscription; differentiation depends on ordering guarantees and filtering efficiency which are undocumented
Implements error recovery patterns for MCP client operations including connection failures, timeout handling, and graceful degradation when servers become unavailable. Provides structured error objects with error codes, messages, and recovery suggestions, enabling agents to implement intelligent fallback strategies.
Unique: unknown — insufficient data on error classification, retry logic, or circuit breaker implementation
vs alternatives: Provides MCP-level error handling; differentiation depends on error classification granularity and built-in resilience patterns which are not documented
Generates TypeScript type definitions and client stubs from MCP server schemas, enabling compile-time type checking for tool parameters, resource URIs, and prompt templates. Uses JSON schema introspection to produce strongly-typed client code that prevents runtime errors from schema mismatches.
Unique: unknown — insufficient data on code generation strategy, schema-to-type mapping rules, or support for complex schema patterns
vs alternatives: Provides MCP-aware code generation for TypeScript; differentiation depends on schema coverage and generated code quality which are undocumented
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 @maz-ui/mcp at 22/100. @maz-ui/mcp leads on ecosystem, while Supabase is stronger on quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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