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Adapters are registered at server startup and dynamically selected based on availability or configuration.","intents":["I want to add a custom movie database as a search source without forking the project","I need to swap between IMDb and TMDB depending on which API is available","I want to implement a local SQLite database as a fallback source when external APIs are down"],"best_for":["teams building custom movie metadata pipelines","developers maintaining multiple data sources and needing unified access","projects requiring offline-first search with optional cloud fallbacks"],"limitations":["Adapter interface is not formally documented — requires reading source code to implement custom adapters","No built-in adapter discovery — sources must be manually registered at server initialization","Adapter errors are not isolated — a failing adapter can block the entire search if not wrapped in try-catch"],"requires":["Node.js 16+","Understanding of MCP tool protocol","Source-specific API credentials or database access for each adapter"],"input_types":["adapter configuration object (source type, credentials, options)"],"output_types":["registered adapter instance with query() and normalize() methods"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-acwinkmovies-search-mcp__cap_3","uri":"capability://data.processing.analysis.normalized.result.schema.mapping.across.heterogeneous.sources","name":"normalized result schema mapping across heterogeneous sources","description":"Transforms raw responses from different movie/TV sources (IMDb HTML, TMDB JSON, custom databases) into a unified, canonical schema with consistent field names, types, and formats. The mapping layer handles optional fields, type coercion, and null-safety, ensuring downstream consumers always receive predictable data structures regardless of source.","intents":["I need consistent field names (e.g., 'releaseYear' not 'year' or 'release_date') across all search results","I want to handle missing fields gracefully without breaking my recommendation logic","I need to ensure all ratings are on the same scale (0-10) regardless of source"],"best_for":["applications consuming movie metadata from multiple sources","teams building data pipelines that require schema consistency","LLM agents that depend on predictable field names for prompt engineering"],"limitations":["Mapping is lossy — source-specific fields not in canonical schema are discarded","No schema versioning — breaking changes to canonical schema require client updates","Type coercion may silently fail for malformed source data (e.g., non-numeric ratings)"],"requires":["Node.js 16+","Source adapters that output raw data matching expected formats"],"input_types":["raw source response (JSON, HTML, database record)"],"output_types":["normalized movie/TV object with canonical fields (title, year, type, rating, genres, etc.)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-acwinkmovies-search-mcp__cap_4","uri":"capability://tool.use.integration.mcp.tool.protocol.integration.for.llm.native.movie.search","name":"mcp tool protocol integration for llm-native movie search","description":"Exposes movie search and validation capabilities as MCP tools that LLM clients (Claude, other MCP-compatible agents) can invoke directly through the Model Context Protocol. The server implements MCP tool definitions with JSON schemas for input validation, handles tool invocation requests, and returns results in MCP-compliant format, enabling seamless integration into LLM agent workflows without custom API clients.","intents":["I want Claude to search for movies directly without me writing custom API code","I need my LLM agent to call movie search as a native tool alongside other capabilities","I want to define tool schemas that Claude understands for movie search parameters"],"best_for":["LLM agent developers building movie recommendation or curation systems","teams using Claude or other MCP-compatible LLMs","developers wanting to expose movie search without building REST APIs"],"limitations":["MCP protocol overhead adds ~50-100ms per tool call vs. direct function calls","Tool schema validation is strict — malformed requests are rejected before reaching search logic","No built-in rate limiting — LLMs can spam tool calls without throttling"],"requires":["Node.js 16+","MCP client (Claude Desktop, LLM framework with MCP support)","MCP server running and accessible to client"],"input_types":["MCP tool invocation request with JSON parameters"],"output_types":["MCP tool result with structured JSON response"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-acwinkmovies-search-mcp__cap_5","uri":"capability://data.processing.analysis.streaming.and.video.resource.metadata.enrichment","name":"streaming and video resource metadata enrichment","description":"Augments movie/TV search results with streaming availability data (which platforms host the content, subscription requirements, rental/purchase options) and video metadata (runtime, quality, subtitles). The enrichment layer queries streaming availability APIs or local databases and merges results into the canonical schema, providing users with actionable information about where to watch.","intents":["I want to know which streaming services have a movie available before recommending it","I need to filter search results to only show content available on Netflix or Prime Video","I want to include rental/purchase prices in my recommendation output"],"best_for":["streaming recommendation engines","content discovery applications","LLM agents helping users find where to watch specific titles"],"limitations":["Streaming availability data is region-specific and changes frequently — cached data may be stale","Not all sources provide streaming availability — enrichment may fail silently for some results","Streaming API access requires additional credentials and may have rate limits"],"requires":["Node.js 16+","Streaming availability API (JustWatch, Reelgood, or custom database)","API credentials for streaming data provider"],"input_types":["normalized movie/TV object with title and year"],"output_types":["enriched movie/TV object with streaming availability array (platform, subscription type, price)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","MCP client implementation (Claude Desktop, LLM framework with MCP support)","API keys for at least one source (IMDb, TMDB, or custom crawler setup)","At least 2 configured sources for meaningful cross-validation","Network connectivity to all configured sources","Understanding of MCP tool protocol","Source-specific API credentials or database access for each adapter","Source adapters that output raw data matching expected formats","MCP client (Claude Desktop, LLM framework with MCP support)","MCP server running and accessible to client"],"failure_modes":["Search latency depends on slowest configured source — no timeout-based fallback documented","Result deduplication relies on title/year matching which may fail for remakes or international titles with different names","No built-in caching layer — repeated searches hit all sources every time","Validation speed is O(n) in number of sources — adding sources increases latency linearly","Cannot detect typos or misspellings in original query — only validates exact matches","Relies on source API availability — if a source is down, confidence scores may be artificially low","Adapter interface is not formally documented — requires reading source code to implement custom adapters","No built-in adapter discovery — sources must be manually registered at server initialization","Adapter errors are not isolated — a failing adapter can block the entire search if not wrapped in try-catch","Mapping is lossy — source-specific fields not in canonical schema are discarded","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.5000000000000001,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:24:09.521Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-acwinkmovies-search-mcp","compare_url":"https://unfragile.ai/compare?artifact=npm-acwinkmovies-search-mcp"}},"signature":"H4/HGYx9b6ah+4YpuZiNkq1WleOdpDJh374SnuAW6gp0KUb2ObSy7dwRrfYFTnX+jtPJz2fX80HUdchftv+rDw==","signedAt":"2026-06-20T23:29:41.103Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-acwinkmovies-search-mcp","artifact":"https://unfragile.ai/npm-acwinkmovies-search-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=npm-acwinkmovies-search-mcp","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"}}