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The server implements MCP's resource protocol to serve component metadata as JSON schemas that describe each component's interface, making it possible for LLMs to reason about component compatibility and correct prop usage during code generation.","intents":["I want my AI assistant to generate Polaris-compliant React code without hallucinating component props","I need the LLM to understand which Polaris components are available and their exact API signatures","I want to ensure generated code uses only valid Polaris component combinations"],"best_for":["Shopify app developers using Claude or other MCP-compatible AI assistants","Teams building internal tools that generate Polaris UI code programmatically","AI-assisted development workflows where component correctness is critical"],"limitations":["Requires MCP-compatible client (Claude Desktop, custom MCP hosts) — not usable with standard OpenAI/Anthropic APIs directly","Schema freshness depends on manual updates when Polaris library versions change","No real-time validation of generated code against actual Polaris runtime behavior"],"requires":["Node.js 16+ (typical MCP server requirement)","MCP-compatible AI assistant or host application","Shopify Polaris package installed or bundled in server"],"input_types":["natural language queries from AI assistant","component name strings","prop name queries"],"output_types":["JSON schema objects describing components","structured metadata about props, types, and defaults","component relationship/hierarchy information"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-polaris-mcp-server__cap_1","uri":"capability://data.processing.analysis.component.property.validation.and.type.inference","name":"component property validation and type inference","description":"Provides structured type information for Polaris component props, enabling AI assistants to understand required vs optional props, prop types (string, boolean, enum, ReactNode), and default values. The server parses or exposes TypeScript type definitions from the Polaris library, allowing LLMs to generate code that respects prop constraints and avoid runtime errors from invalid prop combinations.","intents":["I want the AI to know which props are required vs optional for a given component","I need the LLM to understand valid enum values for props like 'variant' or 'size'","I want to prevent the AI from generating code with incompatible prop types"],"best_for":["Developers building AI code generators for Shopify apps","Teams using Claude or other MCP clients to scaffold Polaris UI","Automated testing systems that validate generated component code"],"limitations":["Type inference accuracy depends on how well Polaris exports TypeScript definitions","Complex union types or conditional props may not be fully expressible in schema format","No runtime validation — schema describes intent but doesn't prevent invalid code execution"],"requires":["Polaris library with exported TypeScript types","MCP server implementation that parses or exposes type metadata","AI assistant capable of interpreting JSON schema constraints"],"input_types":["component name","prop name","type query strings"],"output_types":["JSON schema with type constraints","enum lists for valid prop values","required/optional prop metadata"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-polaris-mcp-server__cap_2","uri":"capability://memory.knowledge.component.usage.example.retrieval.and.pattern.matching","name":"component usage example retrieval and pattern matching","description":"Surfaces curated or extracted code examples for Polaris components through MCP resources, allowing AI assistants to reference real, working usage patterns when generating code. The server likely indexes component examples from Polaris documentation or a curated example set, making them queryable by component name or use case, so LLMs can ground their output in proven patterns rather than generating novel code.","intents":["I want the AI to generate code that follows Polaris best practices and common patterns","I need examples of how to compose multiple Polaris components together","I want the LLM to know idiomatic ways to handle state or callbacks with Polaris components"],"best_for":["Developers new to Polaris who want AI-assisted learning","Teams building code generators that need to maintain consistency with Polaris conventions","Rapid prototyping workflows where example-based generation is faster than first-principles reasoning"],"limitations":["Example coverage depends on what's indexed — niche component combinations may lack examples","Examples may become stale if Polaris API changes and examples aren't updated","LLMs may over-fit to example patterns and generate unnecessarily complex code for simple use cases"],"requires":["Curated or extracted example set (from Polaris docs or community)","Indexing mechanism to map components to examples","MCP resource endpoint to serve examples on demand"],"input_types":["component name","use case description","pattern keywords"],"output_types":["code snippets (JSX/TypeScript)","annotated examples with explanations","component composition patterns"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-polaris-mcp-server__cap_3","uri":"capability://memory.knowledge.real.time.component.library.version.awareness","name":"real-time component library version awareness","description":"Tracks and exposes the version of the Polaris library being served, allowing AI assistants to understand which component APIs and features are available in the current context. The server maintains version metadata and can serve version-specific schemas, enabling LLMs to generate code compatible with the specific Polaris version in use rather than making assumptions based on training data.","intents":["I want the AI to know which Polaris version is being used and generate compatible code","I need to ensure generated code doesn't use components or props that don't exist in our Polaris version","I want to migrate code between Polaris versions and need the AI to understand what changed"],"best_for":["Teams managing multiple Polaris versions across projects","Developers upgrading Polaris and needing AI assistance with API changes","Automated code generation pipelines that must respect version constraints"],"limitations":["Version metadata is only as current as the server's Polaris dependency","No automatic migration guidance — server exposes version info but doesn't suggest code changes","Requires server restart or hot-reload to reflect Polaris version changes"],"requires":["Polaris package installed in server environment","Version detection mechanism (package.json, package metadata, or runtime introspection)","MCP resource endpoint to expose version information"],"input_types":["version query","component name with version context"],"output_types":["version string","version-specific schema metadata","changelog or API diff information"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-polaris-mcp-server__cap_4","uri":"capability://memory.knowledge.component.dependency.and.composition.graph.exposure","name":"component dependency and composition graph exposure","description":"Exposes relationships between Polaris components (e.g., which components can be nested, which components depend on context providers, which components work together idiomatically) as queryable metadata. The server likely analyzes component definitions to infer composition rules, allowing LLMs to understand valid component hierarchies and avoid generating invalid nesting or missing required parent components.","intents":["I want the AI to know which components can be nested inside which other components","I need the LLM to understand that some components require a parent provider or context","I want to generate component trees that respect Polaris composition constraints"],"best_for":["Developers building complex Polaris UIs with AI assistance","Code generators that need to validate component hierarchies","Teams learning Polaris composition patterns through AI-assisted exploration"],"limitations":["Composition rules may be implicit in Polaris and difficult to extract automatically","Complex conditional composition (e.g., 'only valid if parent has prop X') may not be fully expressible","No runtime validation — metadata describes valid patterns but doesn't prevent invalid code"],"requires":["Component metadata with composition information","Analysis mechanism to infer or extract composition rules","MCP resource endpoint to serve composition graph"],"input_types":["component name","composition query (e.g., 'what can contain this component?')"],"output_types":["composition graph as JSON","valid parent/child relationships","required context or provider information"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-polaris-mcp-server__cap_5","uri":"capability://tool.use.integration.mcp.tool.resource.registration.and.discovery","name":"mcp tool/resource registration and discovery","description":"Implements the MCP server protocol to register Polaris-related tools and resources that AI assistants can discover and invoke. The server exposes capabilities through MCP's standard tool and resource endpoints, allowing compatible clients (like Claude Desktop) to understand what operations are available and how to call them with proper parameter schemas.","intents":["I want my AI assistant to discover what Polaris-related operations are available","I need the LLM to understand the exact parameters and return types for each operation","I want to extend the MCP server with custom Polaris-related tools"],"best_for":["MCP-compatible AI assistant users (Claude Desktop, custom MCP hosts)","Developers building custom MCP servers that extend Polaris capabilities","Teams integrating Polaris code generation into broader AI workflows"],"limitations":["Limited to MCP-compatible clients — not usable with standard OpenAI/Anthropic APIs","Tool discovery is static at server startup — dynamic tool registration requires server restart","Parameter schema complexity is limited by JSON schema expressiveness"],"requires":["MCP server implementation (likely Node.js/TypeScript)","MCP-compatible client application","Proper MCP protocol version compatibility"],"input_types":["tool/resource discovery requests (MCP protocol)","tool invocation requests with parameters"],"output_types":["tool/resource metadata (JSON schema)","tool execution results","resource content (component schemas, examples, etc.)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 16+ (typical MCP server requirement)","MCP-compatible AI assistant or host application","Shopify Polaris package installed or bundled in server","Polaris library with exported TypeScript types","MCP server implementation that parses or exposes type metadata","AI assistant capable of interpreting JSON schema constraints","Curated or extracted example set (from Polaris docs or community)","Indexing mechanism to map components to examples","MCP resource endpoint to serve examples on demand","Polaris package installed in server environment"],"failure_modes":["Requires MCP-compatible client (Claude Desktop, custom MCP hosts) — not usable with standard OpenAI/Anthropic APIs directly","Schema freshness depends on manual updates when Polaris library versions change","No real-time validation of generated code against actual Polaris runtime behavior","Type inference accuracy depends on how well Polaris exports TypeScript definitions","Complex union types or conditional props may not be fully expressible in schema format","No runtime validation — schema describes intent but doesn't prevent invalid code execution","Example coverage depends on what's indexed — niche component combinations may lack examples","Examples may become stale if Polaris API changes and examples aren't updated","LLMs may over-fit to example patterns and generate unnecessarily complex code for simple use cases","Version metadata is only as current as the server's Polaris dependency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.37928319529462456,"quality":0.22,"ecosystem":0.55,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":3939,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=polaris-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=polaris-mcp-server"}},"signature":"Cnbc+9sakuliqlUqSyzdQBYkJjTmXvYkVBCRxTbBTFbK1vM2LKPhB2TX+gj9cR2gCwPVxYgB67aD4wV2WHloDQ==","signedAt":"2026-06-15T06:55:16.568Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/polaris-mcp-server","artifact":"https://unfragile.ai/polaris-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=polaris-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"}}