| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Fetches raw component source code from three shadcn/ui implementations (React, Svelte, Vue) by querying GitHub API endpoints for specific component files, with intelligent caching to reduce API calls and fallback to static data when rate limits are exceeded. Uses axios HTTP client with authentication token support for 5,000 req/hour vs 60 req/hour unauthenticated limits, enabling AI assistants to access up-to-date component implementations across framework variants.
Unique: Implements unified GitHub API abstraction layer supporting three distinct shadcn implementations (React/Svelte/Vue) with automatic framework-aware routing and intelligent caching fallback, rather than requiring separate API clients per framework or manual GitHub URL construction
vs alternatives: Provides real-time component source access across three frameworks with built-in rate-limit handling, whereas static documentation or manual GitHub browsing requires manual updates and lacks framework-aware context switching
Exposes static resource lists of all available components, blocks, and themes across supported frameworks through MCP resources endpoint, enabling AI assistants to discover what components exist without making individual GitHub API calls. Uses pre-indexed component metadata (names, descriptions, framework availability) served as JSON resources that can be queried by client tools to populate component pickers or validate component names before requesting source code.
Unique: Pre-indexes component metadata across three framework variants into a single queryable resource list, avoiding per-component API calls and enabling instant component discovery without GitHub API latency or rate-limit concerns
vs alternatives: Faster than querying GitHub API for component lists and more discoverable than requiring users to manually browse GitHub repositories, though less real-time than dynamic API-based indexing
Implements structured error handling using winston logging that captures tool invocation failures, API errors, and rate-limit events with contextual information (component name, framework, error type). Provides detailed error messages to clients through MCP error responses, enabling debugging and graceful error recovery. Logs all significant events (API calls, cache hits, rate limits) for monitoring and troubleshooting production deployments.
Unique: Implements structured logging with winston that captures contextual information about component requests, API calls, and errors, providing observability for production deployments rather than silent failures
vs alternatives: Provides detailed error context and structured logging for debugging, whereas minimal error handling makes production issues difficult to diagnose and monitor
Generates framework-specific installation scripts and setup instructions as MCP templates, routing component installation commands through a multi-framework abstraction layer that translates generic component requests into framework-specific CLI commands (e.g., 'npx shadcn-ui@latest add button' for React vs 'npm add shadcn-svelte' for Svelte). Uses template system to provide step-by-step installation guides with dependency management, peer dependency warnings, and post-install configuration instructions tailored to each framework's ecosystem.
Unique: Implements framework-aware command translation layer that maps generic component installation requests to framework-specific CLI invocations (shadcn-ui vs shadcn-svelte vs shadcn-vue), with built-in peer dependency and configuration guidance per framework
vs alternatives: Eliminates manual framework-specific command lookup and reduces installation errors by providing verified, framework-aware commands, whereas generic installation guides require developers to manually adapt commands for their framework
Extracts demo/example code snippets from shadcn component documentation pages using cheerio HTML parser to parse GitHub-hosted markdown and demo files, exposing runnable code examples that show component usage patterns. Provides AI assistants with concrete usage examples extracted from official documentation, enabling them to generate code that follows established patterns and best practices rather than inferring usage from source code alone.
Unique: Uses cheerio-based HTML parsing to extract executable demo code from GitHub-hosted documentation, providing AI assistants with real usage patterns from official examples rather than requiring inference from component source code
vs alternatives: Provides verified, official usage examples that match documentation, whereas parsing source code alone requires inferring intended usage and may miss common prop combinations shown in demos
Initializes a Model Context Protocol server using @modelcontextprotocol/sdk that exposes tools, resources, and templates through stdio transport, enabling integration with MCP-compatible clients (Claude Desktop, Continue.dev, VS Code extensions). Handles MCP request/response serialization, error handling, and capability advertisement through the standard MCP server capabilities definition, allowing AI tools to discover and invoke component retrieval, installation, and documentation features.
Unique: Implements full MCP server lifecycle using @modelcontextprotocol/sdk with stdio transport, providing standardized protocol handling and capability advertisement that enables seamless integration with any MCP-compatible client without custom protocol implementation
vs alternatives: Standardizes on MCP protocol rather than custom REST/WebSocket APIs, enabling integration with multiple AI tools (Claude, Continue, VS Code) through a single server implementation, whereas tool-specific APIs require separate integrations per platform
Implements a two-tier rate-limiting strategy that uses authenticated GitHub API tokens (5,000 req/hour) when available and falls back to unauthenticated limits (60 req/hour) with smart caching to reduce API calls. When rate limits are exceeded, the server automatically serves pre-cached component data instead of failing, ensuring graceful degradation and continuous availability even under high load. Uses axios interceptors to track remaining API quota and proactively switch to cached responses before hitting hard limits.
Unique: Implements proactive rate-limit management with automatic fallback to pre-cached component data, preventing service degradation when GitHub API quota is exhausted, rather than failing hard when limits are hit
vs alternatives: Provides continuous availability under high load by gracefully degrading to cached data, whereas naive API clients fail entirely when rate limits are exceeded, and simple caching without quota awareness cannot prevent hitting limits
Provides a unified abstraction layer that maps generic component requests to framework-specific implementations (React, Svelte, Vue) by routing requests through a framework-aware dispatcher that handles differences in component APIs, file structures, and installation methods. Abstracts away framework-specific details so clients can request 'Button component' without specifying framework-specific paths, import syntax, or installation commands, with the server automatically translating to the correct framework variant.
Unique: Implements unified component request interface that abstracts framework differences through a routing dispatcher, enabling single-request access to React/Svelte/Vue variants rather than requiring framework-specific tool invocations
vs alternatives: Simplifies multi-framework support by hiding routing logic from clients, whereas separate tools per framework require clients to implement framework selection logic and duplicate request handling
+3 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 shadcn-ui-mcp-server at 37/100.
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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