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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.","intents":["I need to show an AI assistant the exact source code of a Button component in React, Svelte, and Vue so it can generate framework-specific implementations","I want to cache component source code locally to avoid hitting GitHub API rate limits during development","I need to retrieve component source even when GitHub API is rate-limited by falling back to pre-cached static data"],"best_for":["AI coding assistants (Claude, Continue.dev) that need real-time component context","IDE extensions building multi-framework UI scaffolding tools","Development teams migrating between React/Svelte/Vue who need consistent component APIs"],"limitations":["Unauthenticated requests capped at 60/hour per IP — requires GitHub Personal Access Token for production use","Caching strategy not specified in architecture — cache invalidation timing unknown","Only supports shadcn/ui ecosystem — cannot retrieve components from other UI libraries (Material-UI, Chakra, etc.)","Depends on GitHub repository structure stability — breaking changes to repo layout would require code updates"],"requires":["Node.js 18+ (implied by MCP SDK requirements)","GitHub Personal Access Token (optional but recommended for >60 req/hour)","Network access to github.com API endpoints","@modelcontextprotocol/sdk ^1.16.0","axios ^1.8.4"],"input_types":["component name (string)","framework identifier (react|svelte|vue)","optional: GitHub API token (string)"],"output_types":["raw component source code (TypeScript/JavaScript/Svelte/Vue)","component metadata (name, path, framework)","cached response indicator"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_1","uri":"capability://search.retrieval.component.metadata.and.documentation.indexing","name":"component metadata and documentation indexing","description":"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.","intents":["I want to show users a list of all available shadcn components so they can pick one to generate code for","I need to validate that a component name exists before attempting to fetch its source code","I want to know which components are available in each framework (React vs Svelte vs Vue) to guide user selection"],"best_for":["UI code generation tools that need component discovery without API overhead","AI assistants building component selection interfaces or validation logic","Teams building internal component catalogs with shadcn as the source of truth"],"limitations":["Static indexing — component lists are pre-built and not updated in real-time; new components require server restart or manual index refresh","No search or filtering capability — clients must implement their own search logic over the full component list","No component dependency information — does not expose which components depend on others (e.g., Dialog depends on Primitive)","Limited metadata — only includes component names and framework availability, not detailed prop schemas or usage examples"],"requires":["MCP client supporting resources endpoint","No external dependencies — served from bundled static data","Server must be running to access resource lists"],"input_types":["none — resources are static and queried by client without parameters"],"output_types":["JSON array of component objects with structure: {name: string, frameworks: string[], description?: string}","JSON array of block objects (pre-built component combinations)","JSON array of theme objects (color/styling variants)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_10","uri":"capability://safety.moderation.error.handling.and.logging.with.structured.output","name":"error handling and logging with structured output","description":"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.","intents":["I want detailed error messages when component retrieval fails so I can understand what went wrong","I need to monitor server health and API usage patterns in production","I want to debug issues with component retrieval or installation by reviewing structured logs"],"best_for":["Production deployments that need observability and error tracking","Development teams debugging component retrieval issues","Operations teams monitoring MCP server health and API quota usage"],"limitations":["Logging is local to server process — no centralized log aggregation without external setup","Log verbosity is not configurable — cannot adjust logging level at runtime","No structured log export — logs are written to console/file without structured format for log aggregation tools","Error context is limited to MCP request scope — cross-request error correlation is not supported"],"requires":["winston ^3.x for logging","File system access for log file writing (if file logging is enabled)","Log aggregation tool (ELK, Datadog, etc.) for production monitoring (optional)"],"input_types":["error events (tool failures, API errors, rate limits)","informational events (API calls, cache operations)"],"output_types":["structured log entries (timestamp, level, message, context)","MCP error responses with error codes and descriptions","log files (if file logging is enabled)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_2","uri":"capability://automation.workflow.framework.aware.component.installation.template.generation","name":"framework-aware component installation template generation","description":"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.","intents":["I want to generate the exact npm/npx command to install a specific component in React, Svelte, or Vue","I need installation instructions that include all required peer dependencies and configuration steps for a given framework","I want to provide users with copy-paste-ready installation commands that work in their specific project setup"],"best_for":["Code generation tools that output component installation steps alongside generated code","AI assistants helping developers set up shadcn components in new projects","Documentation generators that need framework-specific installation guides"],"limitations":["Templates are static and pre-defined — cannot generate custom installation logic for project-specific configurations","No validation of project setup — does not check if npm/node is installed or if project is properly initialized","Framework detection is manual — requires explicit framework selection; cannot auto-detect from package.json","No post-installation verification — does not confirm that components were installed successfully or run integration tests"],"requires":["MCP client supporting templates endpoint","Framework identifier (react|svelte|vue) must be specified","Component name must be valid and exist in target framework"],"input_types":["component name (string)","target framework (react|svelte|vue)","optional: project root path (string)"],"output_types":["installation command string (npm/npx command)","step-by-step installation guide (markdown or plain text)","dependency list with versions","post-installation configuration instructions"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_3","uri":"capability://code.generation.editing.component.demo.code.extraction.and.rendering.context","name":"component demo code extraction and rendering context","description":"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.","intents":["I want to show users example code demonstrating how to use a component with common prop combinations","I need to extract the demo code from shadcn documentation so I can generate similar usage patterns in generated code","I want to provide context about component behavior by showing how it's used in official examples"],"best_for":["Code generation tools that need usage pattern context to generate realistic component implementations","AI assistants building component usage documentation or tutorials","Interactive code editors that want to show live demo examples alongside generated code"],"limitations":["Demo extraction depends on consistent HTML structure — changes to shadcn documentation layout would break parsing","Only extracts static code — cannot capture interactive behavior or state management patterns from live demos","Limited to documented examples — if a component has no official demo, no example code is available","cheerio parsing adds ~50-100ms latency per demo extraction due to HTML parsing overhead"],"requires":["cheerio ^1.0.0 for HTML parsing","Component must have official demo documentation in GitHub repository","Network access to GitHub raw content endpoints"],"input_types":["component name (string)","framework identifier (react|svelte|vue)"],"output_types":["demo code snippet (TypeScript/JavaScript/Svelte/Vue)","component props used in demo (structured data)","demo description or title (string)"],"categories":["code-generation-editing","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_4","uri":"capability://tool.use.integration.mcp.protocol.server.initialization.and.stdio.transport","name":"mcp protocol server initialization and stdio transport","description":"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.","intents":["I want to connect my AI assistant (Claude, Continue.dev) to shadcn component data through the MCP protocol","I need to expose component tools and resources to IDE extensions that support MCP","I want to standardize how AI tools access shadcn components across different development environments"],"best_for":["AI development tool builders integrating shadcn support into Claude Desktop or Continue.dev","IDE extension developers adding MCP server support to VS Code or other editors","Teams standardizing on MCP for AI-assisted development workflows"],"limitations":["stdio transport only — no HTTP/WebSocket support; requires process-level integration","Single server instance per process — cannot run multiple MCP servers in same Node process","Error handling is basic — unhandled exceptions in tool handlers may crash the server without graceful recovery","No built-in authentication — relies on client-side access control; any process with stdio access can invoke tools"],"requires":["Node.js 18+ (for ES modules and async/await support)","@modelcontextprotocol/sdk ^1.16.0","MCP-compatible client (Claude Desktop, Continue.dev, custom MCP client)","stdio file descriptors available (stdin/stdout)"],"input_types":["MCP protocol messages (JSON-RPC format)","tool invocation requests with parameters","resource queries"],"output_types":["MCP protocol responses (JSON-RPC format)","tool results (component source, installation commands, etc.)","resource listings","error messages with MCP error codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_5","uri":"capability://automation.workflow.intelligent.github.api.rate.limit.handling.with.fallback.caching","name":"intelligent github api rate-limit handling with fallback caching","description":"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.","intents":["I want to use the MCP server in production without worrying about GitHub API rate limits blocking component retrieval","I need to provide a GitHub token to increase my rate limit from 60 to 5,000 requests per hour","I want the server to automatically fall back to cached data when rate limits are exceeded instead of failing"],"best_for":["Production AI assistants serving multiple users where API quota is a concern","Development teams running continuous code generation workflows that make frequent component requests","Environments where GitHub API availability is unreliable and fallback data is critical"],"limitations":["Fallback cache is static — cached data may be stale if components have been updated in GitHub","No cache invalidation strategy documented — unclear when cached data is refreshed or how to force updates","Rate limit tracking is per-server instance — distributed deployments cannot share quota across instances","GitHub token must be provided as environment variable — no runtime token rotation or management UI","Unauthenticated limit of 60 req/hour is insufficient for multi-user scenarios — requires token for production use"],"requires":["GitHub Personal Access Token (optional but recommended) with 'public_repo' scope","Environment variable: GITHUB_TOKEN (if using authenticated requests)","axios ^1.8.4 for HTTP client with interceptor support","Network access to api.github.com"],"input_types":["GitHub API token (string, optional)","component request (triggers API call or cache lookup)"],"output_types":["component data (from API or cache)","rate-limit status (remaining quota, reset time)","cache hit/miss indicator"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_6","uri":"capability://tool.use.integration.multi.framework.component.abstraction.and.routing","name":"multi-framework component abstraction and routing","description":"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.","intents":["I want to request a component without knowing the exact framework-specific file path or import syntax","I need to generate code for multiple frameworks from a single component request by routing through a framework abstraction","I want to hide framework differences from the client so it can work with shadcn components generically"],"best_for":["Multi-framework code generation tools that support React, Svelte, and Vue from a single codebase","AI assistants that need to work with components across frameworks without framework-specific logic","Teams building framework-agnostic component libraries or design systems"],"limitations":["Abstraction only covers three frameworks — adding new frameworks requires code changes to routing logic","Component APIs may differ between frameworks — abstraction cannot hide semantic differences (e.g., prop names, event handling)","No automatic API translation — if a component has different props in React vs Svelte, client must handle both variants","Framework selection is explicit — cannot auto-detect target framework from project context"],"requires":["Framework identifier (react|svelte|vue) must be specified in request","Component name must exist in target framework","Routing logic must be implemented for each new framework variant"],"input_types":["component name (string)","target framework (react|svelte|vue)","optional: component variant or props"],"output_types":["framework-specific component source code","framework-specific installation command","framework-specific import statement","framework-specific usage example"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_7","uri":"capability://code.generation.editing.component.source.code.parsing.and.schema.validation","name":"component source code parsing and schema validation","description":"Parses retrieved component source code using zod runtime type validation to extract and validate component prop schemas, ensuring type safety and enabling AI assistants to understand component APIs without manual inspection. Validates component structure against expected patterns (exports, prop definitions, TypeScript types) to catch malformed or incomplete components before exposing them to clients, providing structured component metadata (prop names, types, defaults, required status) that can be used for code generation and validation.","intents":["I want to extract prop schemas from component source code so I can generate type-safe component usage","I need to validate that a component has the expected structure before using it in code generation","I want to provide users with autocomplete suggestions for component props based on parsed schemas"],"best_for":["Code generation tools that need to validate component APIs before generating usage code","IDE extensions building component prop autocomplete and type checking","AI assistants that need structured component metadata for intelligent code generation"],"limitations":["Parsing depends on consistent TypeScript/JSX syntax — components with unusual patterns may fail validation","zod validation adds ~20-50ms latency per component parse due to schema checking overhead","Cannot extract runtime behavior — only static type information; dynamic prop validation at runtime is not captured","Limited to TypeScript components — JavaScript-only components may have incomplete type information","No support for complex prop types — union types, generics, and conditional types may not parse correctly"],"requires":["zod ^3.24.2 for schema validation","Component source code must be valid TypeScript/JSX","Component must export prop interface or type definition"],"input_types":["component source code (TypeScript/JavaScript string)","component name (string, for validation context)"],"output_types":["parsed prop schema (structured object with prop names, types, defaults)","validation result (pass/fail with error details)","component metadata (exports, dependencies, required props)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_8","uri":"capability://planning.reasoning.conversational.component.usage.prompts.and.patterns","name":"conversational component usage prompts and patterns","description":"Exposes MCP prompts endpoint with pre-built conversational templates that guide AI assistants through component selection, configuration, and usage patterns. Provides structured prompts that establish context about component capabilities, common use cases, prop combinations, and best practices, enabling AI assistants to generate more contextually appropriate code without requiring users to provide detailed specifications.","intents":["I want the AI assistant to understand common component usage patterns without me having to explain them","I need conversational guidance on which component to use for a given UI requirement","I want the AI to suggest best practices and common prop combinations for components"],"best_for":["AI assistants (Claude, Continue.dev) that benefit from structured context about component usage","Interactive code generation tools that guide users through component selection","Teams building component usage documentation or style guides"],"limitations":["Prompts are static and pre-defined — cannot adapt to project-specific conventions or custom components","Limited to documented patterns — if a component has uncommon usage patterns, prompts may not cover them","No learning or feedback loop — prompts do not improve based on user interactions or generated code quality","Prompt effectiveness depends on AI model — different models may interpret prompts differently"],"requires":["MCP client supporting prompts endpoint","AI model capable of following structured prompts (GPT-4, Claude, etc.)"],"input_types":["none — prompts are static and queried by client"],"output_types":["prompt text (markdown or plain text)","prompt arguments (component name, use case, etc.)","example usage patterns"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-jpisnice-shadcn-ui-mcp-server__cap_9","uri":"capability://code.generation.editing.block.and.theme.variant.discovery.and.retrieval","name":"block and theme variant discovery and retrieval","description":"Extends component retrieval to support pre-built component combinations (blocks) and theme variants, enabling AI assistants to access not just individual components but also curated multi-component layouts and styling variants. Blocks are documented component combinations (e.g., a card with header, content, and footer) that can be retrieved as complete units, while themes represent color schemes and styling variants that can be applied across components.","intents":["I want to retrieve a pre-built component combination (block) like a card layout instead of assembling components manually","I need to discover available theme variants so I can apply consistent styling across generated components","I want to generate complete UI sections using blocks rather than individual components"],"best_for":["Code generation tools building complete UI sections rather than individual components","AI assistants generating styled, multi-component layouts","Design system tools that need to expose pre-built patterns and themes"],"limitations":["Block availability depends on shadcn documentation — not all component combinations are documented as blocks","Blocks are static combinations — cannot generate custom block combinations dynamically","Theme variants are limited to documented themes — custom theme generation is not supported","No theme customization — themes are retrieved as-is without ability to modify colors or spacing"],"requires":["Block and theme metadata must be indexed in component database","GitHub API access for retrieving block source code"],"input_types":["block name (string)","theme name (string)","framework identifier (react|svelte|vue)"],"output_types":["block source code (multi-component layout)","theme configuration (color scheme, styling variables)","block metadata (components used, dependencies)"],"categories":["code-generation-editing","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+ (implied by MCP SDK requirements)","GitHub Personal Access Token (optional but recommended for >60 req/hour)","Network access to github.com API endpoints","@modelcontextprotocol/sdk ^1.16.0","axios ^1.8.4","MCP client supporting resources endpoint","No external dependencies — served from bundled static data","Server must be running to access resource lists","winston ^3.x for logging","File system access for log file writing (if file logging is enabled)"],"failure_modes":["Unauthenticated requests capped at 60/hour per IP — requires GitHub Personal Access Token for production use","Caching strategy not specified in architecture — cache invalidation timing unknown","Only supports shadcn/ui ecosystem — cannot retrieve components from other UI libraries (Material-UI, Chakra, etc.)","Depends on GitHub repository structure stability — breaking changes to repo layout would require code updates","Static indexing — component lists are pre-built and not updated in real-time; new components require server restart or manual index refresh","No search or filtering capability — clients must implement their own search logic over the full component list","No component dependency information — does not expose which components depend on others (e.g., Dialog depends on Primitive)","Limited metadata — only includes component names and framework availability, not detailed prop schemas or usage examples","Logging is local to server process — no centralized log aggregation without external setup","Log verbosity is not configurable — cannot adjust logging level at runtime","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.529329852340575,"quality":0.47,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:38.364Z","last_commit":"2026-03-17T08:17:56Z"},"community":{"stars":2757,"forks":289,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-jpisnice-shadcn-ui-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mcp-jpisnice-shadcn-ui-mcp-server"}},"signature":"G6wfEfPZZS5wWeHgg4PR9qk0F2y1xu3AkfmgDe+Fob+PGgq1w5Fvy/EWkoAnYeqUCZBh1U3hV8WcmiDvSHN5Bw==","signedAt":"2026-06-22T07:02:45.826Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-jpisnice-shadcn-ui-mcp-server","artifact":"https://unfragile.ai/mcp-jpisnice-shadcn-ui-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-jpisnice-shadcn-ui-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}