| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides strongly-typed boilerplate and utilities for building MCP servers in TypeScript, handling the protocol handshake, request/response serialization, and lifecycle management. Uses TypeScript generics and discriminated unions to enforce type safety across tool definitions, resource handlers, and prompt templates, reducing runtime errors and enabling IDE autocomplete for MCP protocol compliance.
Unique: Provides Clerk-aware MCP server scaffolding with built-in authentication context propagation, allowing servers to access Clerk user/organization data without manual token management or context threading
vs alternatives: Faster MCP server setup than raw protocol implementation with automatic Clerk auth integration, vs generic MCP libraries that require separate auth plumbing
Abstracts MCP client creation across multiple transport layers (stdio, HTTP, WebSocket) and LLM providers (OpenAI, Anthropic, custom), handling connection pooling, reconnection logic, and provider-specific capability negotiation. Uses a factory pattern with pluggable transport adapters and provider-specific message formatters to normalize tool calling across different LLM APIs.
Unique: Provides unified client API that normalizes tool calling across OpenAI, Anthropic, and other providers, translating between provider-specific function calling schemas and MCP tool definitions automatically
vs alternatives: Eliminates provider lock-in vs building separate clients per provider; faster multi-provider experimentation than manual schema translation
Validates tool definitions against MCP schema specifications and converts between MCP tool schemas and provider-specific formats (OpenAI function calling, Anthropic tool use). Uses JSON Schema validation with custom error messages and provides bidirectional converters that preserve parameter constraints, descriptions, and required fields across format boundaries.
Unique: Bidirectional schema conversion with constraint preservation — converts OpenAI/Anthropic tool definitions to MCP while maintaining parameter validation rules, descriptions, and required field metadata
vs alternatives: Eliminates manual schema rewriting vs copy-pasting tool definitions per provider; catches schema errors at validation time vs runtime failures
Automatically injects Clerk user/organization context into MCP request messages and extracts it from responses, enabling MCP servers to access authenticated user data without explicit token passing. Implements context middleware that intercepts MCP calls, enriches them with Clerk session tokens and user metadata, and validates responses against Clerk permissions.
Unique: Clerk-native MCP middleware that transparently propagates Clerk user/org context through MCP tool calls without requiring explicit token passing in tool parameters, enabling authorization checks at the MCP layer
vs alternatives: Simpler than manual token threading through tool parameters; Clerk-specific vs generic auth middleware that requires custom integration
Provides TypeScript interfaces and decorators for defining MCP resources (files, documents, data) and prompt templates with compile-time type checking. Uses discriminated unions and generic constraints to ensure resource handlers return correct types and prompt templates have valid variable substitution, with IDE autocomplete for resource URIs and template variables.
Unique: Decorator-based resource and prompt definition with compile-time variable validation — catches missing or misspelled template variables before runtime, unlike string-based template systems
vs alternatives: Faster development with IDE autocomplete vs manual resource URI management; compile-time safety vs runtime template errors
Wraps MCP tool handlers with automatic error catching, serialization, and protocol-compliant error responses. Converts JavaScript/TypeScript exceptions into MCP error objects with proper error codes, messages, and optional stack traces, and validates that all responses conform to MCP protocol specifications before sending.
Unique: Automatic error wrapping with MCP protocol compliance validation — catches exceptions in tool handlers and converts them to spec-compliant error responses without manual serialization
vs alternatives: Prevents protocol violations that break clients vs manual error handling; automatic validation vs hoping responses are correct
Supports deploying the same MCP server across multiple transport layers (stdio for local processes, HTTP for REST-like access, WebSocket for bidirectional streaming) using a transport-agnostic server implementation. Uses adapter pattern to normalize message handling across transports and provides configuration for each transport's specific requirements (port binding, CORS, authentication).
Unique: Single server implementation deployable across stdio, HTTP, and WebSocket transports using adapter pattern — eliminates transport-specific code duplication and enables runtime transport selection
vs alternatives: Faster multi-transport deployment vs writing separate servers per transport; flexible deployment vs locked-in transport choice
Caches tool execution results with configurable time-to-live (TTL) and cache key generation based on tool name and parameters. Uses in-memory or Redis-backed storage (configurable) to avoid redundant tool invocations when the same parameters are requested multiple times, with cache invalidation hooks for tools that produce time-sensitive results.
Unique: Transparent tool result caching with configurable TTL and Redis support — intercepts tool calls and returns cached results without modifying tool handler code, with optional distributed cache for multi-instance deployments
vs alternatives: Reduces tool call latency and API costs vs no caching; distributed Redis support vs in-memory-only caching for single-instance deployments
+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 @clerk/mcp-tools 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