{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-context-awesome","slug":"context-awesome","name":"context-awesome","type":"mcp","url":"https://github.com/bh-rat/context-awesome","page_url":"https://unfragile.ai/context-awesome","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-context-awesome__cap_0","uri":"capability://search.retrieval.natural.language.awesome.list.section.discovery.with.confidence.scoring","name":"natural language awesome list section discovery with confidence scoring","description":"Searches across 8,500+ curated GitHub awesome lists using the find_awesome_section MCP tool, which accepts natural language queries and returns matching sections ranked by confidence scores. The tool communicates with a backend API (api.context-awesome.com) that maintains an indexed, searchable corpus of awesome list metadata, enabling agents to discover relevant resource categories without knowing exact list names or section titles. Confidence scoring helps agents prioritize results and make informed decisions about which sections to retrieve items from.","intents":["Find relevant awesome lists and sections for a given topic using natural language","Discover curated resource categories without knowing exact GitHub repository names","Get ranked results with confidence scores to prioritize which sections to explore further","Enable agents to autonomously locate resource collections matching user queries"],"best_for":["AI agents building context-aware recommendations","Development tools needing to surface curated resources to users","Teams building knowledge discovery workflows"],"limitations":["Search quality depends on backend API indexing freshness — awesome lists may lag behind GitHub updates","Confidence scores are backend-computed; no transparency into scoring algorithm from client side","Natural language queries may return false positives if query terms are ambiguous or overloaded across multiple domains"],"requires":["MCP client integration (Claude, Cursor, VS Code with MCP support)","Network connectivity to api.context-awesome.com or local MCP server instance","Node.js 18+ if running local server"],"input_types":["text (natural language query string)"],"output_types":["structured JSON with section metadata, list names, confidence scores"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-awesome__cap_1","uri":"capability://data.processing.analysis.token.aware.paginated.item.retrieval.from.awesome.list.sections","name":"token-aware paginated item retrieval from awesome list sections","description":"Implements the get_awesome_items MCP tool that retrieves actual resource items from discovered awesome list sections with built-in pagination and token-aware context management. The tool accepts section identifiers from find_awesome_section results and returns paginated batches of items, allowing agents to control how many items are fetched to stay within LLM context windows. Pagination is designed to be transparent to the agent — it can request items in chunks and iterate through results without managing offsets manually.","intents":["Fetch actual resource items (links, descriptions, metadata) from a specific awesome list section","Retrieve items in paginated batches to respect LLM context token limits","Get structured item data (title, URL, description) for downstream processing or presentation","Iterate through large sections without loading entire lists into memory or context"],"best_for":["Agents building curated resource recommendations with token budget constraints","Tools that need to present paginated results to users","Systems integrating awesome lists into RAG or knowledge base pipelines"],"limitations":["Pagination state is stateless — agents must track page tokens or offsets themselves; no server-side session management","Item metadata richness depends on awesome list source quality — some lists have minimal descriptions","Large sections (1000+ items) require multiple API calls, adding latency for comprehensive retrieval"],"requires":["Valid section identifier from find_awesome_section results","MCP client with tool-calling support","Network connectivity to backend API or local MCP server"],"input_types":["structured JSON with section ID, optional pagination parameters (limit, offset/page token)"],"output_types":["structured JSON array of items with title, URL, description, and pagination metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-awesome__cap_2","uri":"capability://tool.use.integration.mcp.protocol.implementation.with.multi.transport.support","name":"mcp protocol implementation with multi-transport support","description":"Implements the Model Context Protocol (MCP) server specification in TypeScript (src/index.ts), exposing the find_awesome_section and get_awesome_items tools through a standardized interface. The server supports three distinct transport mechanisms — stdio (for local process communication), HTTP (for REST-like access), and SSE (Server-Sent Events for streaming responses) — allowing flexible integration with different AI clients and deployment architectures. Transport selection is configured via CLI arguments, enabling the same server code to run in multiple deployment contexts without modification.","intents":["Expose awesome list tools to Claude, Cursor, VS Code, and other MCP-compatible clients","Support multiple deployment models (local, remote, containerized) with transport abstraction","Enable standardized tool calling across heterogeneous AI client ecosystems","Provide a bridge between AI agents and the awesome list backend API"],"best_for":["Teams building MCP-native AI applications","Organizations deploying AI tools across multiple client platforms","Developers integrating awesome lists into agent workflows"],"limitations":["MCP is a relatively new protocol — client support varies; not all AI tools support MCP yet","Transport abstraction adds ~50-100ms overhead per request due to serialization/deserialization","Stdio transport is single-threaded and blocking; not suitable for high-concurrency scenarios","HTTP transport requires explicit port configuration and firewall rules in networked environments"],"requires":["Node.js 18+","TypeScript compiler or pre-built JavaScript distribution","MCP-compatible client (Claude, Cursor, VS Code with MCP extension)","For HTTP/SSE: network connectivity and port availability"],"input_types":["MCP tool call requests (JSON-RPC format)"],"output_types":["MCP tool call responses (JSON-RPC format with results or errors)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-awesome__cap_3","uri":"capability://tool.use.integration.backend.api.client.with.error.handling.and.response.normalization","name":"backend api client with error handling and response normalization","description":"The AwesomeContextAPIClient (src/api-client.ts) abstracts communication with the backend api.context-awesome.com service, handling HTTP requests, error recovery, token management, and response normalization. It implements retry logic for transient failures, normalizes API responses into consistent TypeScript types, and manages authentication tokens if required. This abstraction isolates the MCP server from backend API changes and provides a single point for implementing cross-cutting concerns like rate limiting, caching, or circuit breaking.","intents":["Communicate reliably with the awesome list backend API from the MCP server","Handle transient network failures and API errors gracefully","Normalize heterogeneous backend responses into consistent TypeScript types","Manage authentication and token lifecycle for backend API access"],"best_for":["MCP server implementations requiring backend service integration","Teams building resilient API clients with error handling","Systems needing to abstract backend API complexity from tool implementations"],"limitations":["Retry logic is fixed (no exponential backoff configuration exposed); may not suit all failure scenarios","No built-in caching — repeated queries to the same section hit the backend API each time","Response normalization assumes backend API schema stability; breaking changes require client updates","Token management is basic; no refresh token rotation or OAuth2 flow support"],"requires":["Network connectivity to api.context-awesome.com","Valid API endpoint URL (configurable via environment or constructor)","Optional: API key if backend requires authentication"],"input_types":["structured request objects (query strings, section IDs, pagination parameters)"],"output_types":["normalized TypeScript objects matching src/types.ts contracts"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-awesome__cap_4","uri":"capability://automation.workflow.docker.containerization.with.smithery.deployment.integration","name":"docker containerization with smithery deployment integration","description":"Packages the MCP server as a Docker container (Dockerfile) with Smithery configuration (smithery.yaml) for managed deployment on the Smithery platform. The container includes Node.js runtime, TypeScript compilation, and all dependencies, enabling one-command deployment to cloud infrastructure. Smithery configuration specifies runtime settings, environment variables, and port bindings, abstracting infrastructure details from developers.","intents":["Deploy the MCP server to cloud infrastructure without manual server setup","Run the server in isolated, reproducible container environments","Enable managed deployment with automatic scaling and monitoring via Smithery","Simplify multi-environment deployments (dev, staging, production)"],"best_for":["Teams deploying MCP servers to managed cloud platforms","Organizations requiring containerized, reproducible deployments","Projects needing to separate development and production environments"],"limitations":["Docker adds ~2-5 second startup overhead vs. native Node.js execution","Smithery is a specific platform; configuration is not portable to other container orchestration systems (Kubernetes, Docker Compose)","Container image size includes full Node.js runtime; not suitable for serverless/Lambda deployments","Dockerfile assumes Linux base image; Windows-native deployments require separate configuration"],"requires":["Docker runtime (Docker Desktop, Docker Engine, or container orchestration platform)","Smithery account and CLI for managed deployment","Port availability (default 3000 for HTTP transport)"],"input_types":["Dockerfile build context, smithery.yaml configuration"],"output_types":["Docker image, deployed container instance on Smithery"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-awesome__cap_5","uri":"capability://code.generation.editing.type.safe.request.response.contracts.across.all.layers","name":"type-safe request/response contracts across all layers","description":"Defines comprehensive TypeScript type contracts (src/types.ts) for all requests, responses, and configuration objects used throughout the MCP server, tool implementations, and API client. These types enforce compile-time safety across the entire request/response pipeline, preventing type mismatches between the MCP protocol layer, tool implementations, and backend API client. Type definitions include request schemas (query parameters, section IDs), response schemas (items, sections, pagination metadata), and configuration types (transport settings, API endpoints).","intents":["Ensure type safety across MCP server, tools, and backend API client","Enable IDE autocomplete and refactoring support for tool parameters and responses","Document expected request/response shapes for tool consumers","Catch type errors at compile time rather than runtime"],"best_for":["TypeScript-based MCP server implementations","Teams prioritizing type safety and developer experience","Projects with multiple contributors needing shared type contracts"],"limitations":["TypeScript types are compile-time only; runtime validation requires separate schema validation library (not included)","Type definitions must be manually kept in sync with backend API schema changes","Complex nested types can reduce readability; no built-in documentation generation from types","Type safety is only enforced in TypeScript; clients in other languages must implement their own type contracts"],"requires":["TypeScript compiler (tsc) or build tool with TypeScript support","TypeScript 4.5+ for advanced type features used in definitions"],"input_types":["TypeScript source files with type annotations"],"output_types":["Compiled JavaScript with type information stripped; .d.ts type declaration files"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-awesome__cap_6","uri":"capability://tool.use.integration.stateless.request.routing.and.tool.registration","name":"stateless request routing and tool registration","description":"The MCP server (src/index.ts) implements stateless request routing that maps incoming MCP tool calls to handler functions for find_awesome_section and get_awesome_items. Tool registration is declarative — each tool is defined with its name, description, input schema, and handler function — enabling the server to automatically expose tools to clients without manual routing logic. Routing is stateless, meaning each request is processed independently without maintaining session state, simplifying deployment and scaling.","intents":["Route incoming MCP tool calls to appropriate handler functions","Register tools with MCP clients with metadata (descriptions, input schemas)","Enable clients to discover available tools and their parameters","Process tool calls independently without session state management"],"best_for":["MCP server implementations with multiple tools","Stateless service architectures requiring horizontal scaling","Teams building tool-rich AI applications"],"limitations":["Stateless design means no built-in session management; agents cannot maintain state across requests","Tool registration is static at server startup; dynamic tool registration requires server restart","No built-in middleware or interceptor pattern; cross-cutting concerns (logging, metrics) require manual implementation","Error handling is basic; no standardized error codes or structured error responses"],"requires":["MCP protocol understanding for tool definition format","Handler functions matching expected input/output signatures"],"input_types":["MCP tool call requests with tool name and parameters"],"output_types":["MCP tool call responses with results or error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-awesome__cap_7","uri":"capability://tool.use.integration.multi.protocol.transport.abstraction.stdio.http.sse","name":"multi-protocol transport abstraction (stdio, http, sse)","description":"Abstracts the underlying transport mechanism (stdio, HTTP, or SSE) behind a unified interface, allowing the same MCP server code to operate across different deployment contexts. Stdio transport uses standard input/output for local process communication (suitable for VS Code extensions). HTTP transport exposes the server as a REST-like endpoint (suitable for remote clients). SSE transport uses Server-Sent Events for streaming responses (suitable for long-lived connections). Transport selection is configured via CLI arguments without code changes.","intents":["Deploy the same MCP server code to multiple client types (local, remote, streaming)","Support different network topologies (local process, HTTP client, event stream)","Enable flexible deployment without reimplementing server logic for each transport","Simplify client integration by supporting their preferred transport mechanism"],"best_for":["MCP servers targeting multiple client types","Organizations with heterogeneous deployment requirements","Teams building tools that work both locally and remotely"],"limitations":["Transport abstraction adds serialization/deserialization overhead (~50-100ms per request)","Stdio transport is single-threaded and blocking; not suitable for concurrent requests","HTTP transport requires explicit port configuration and firewall rules","SSE transport has limited browser compatibility and requires long-lived connections","No automatic transport selection; clients must know which transport to use"],"requires":["Node.js 18+ with stream and HTTP module support","CLI argument or environment variable specifying transport type","For HTTP/SSE: network connectivity and port availability"],"input_types":["Transport-specific request format (JSON-RPC for stdio/HTTP, event stream for SSE)"],"output_types":["Transport-specific response format (JSON-RPC for stdio/HTTP, event stream for SSE)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-context-awesome__cap_8","uri":"capability://memory.knowledge.awesome.list.aggregation.and.indexing.at.backend","name":"awesome list aggregation and indexing at backend","description":"The backend service (api.context-awesome.com) aggregates and indexes 8,500+ GitHub awesome lists containing 1M+ items, maintaining a searchable corpus that the MCP server queries. The backend handles list discovery, parsing, indexing, and search functionality, exposing HTTP endpoints that the MCP client consumes. While the MCP server itself doesn't implement indexing, it provides agents access to the backend's aggregated, indexed data through the find_awesome_section and get_awesome_items tools.","intents":["Access a unified, searchable index of 8,500+ awesome lists without maintaining local copies","Query the aggregated awesome list corpus using natural language","Retrieve items from indexed sections with confidence-ranked results","Leverage community-curated resources at scale"],"best_for":["Agents needing broad resource discovery across multiple domains","Applications building knowledge bases from curated lists","Teams leveraging community-maintained awesome lists without manual curation"],"limitations":["Backend indexing freshness depends on update frequency; awesome lists may lag behind GitHub","Search quality depends on backend indexing algorithm; no control over ranking from client side","Backend availability is a single point of failure; no built-in fallback or offline mode","Data freshness and coverage are opaque to clients; no visibility into which lists are indexed or when they were last updated","Backend API rate limits may apply; no published limits or quota management from client side"],"requires":["Network connectivity to api.context-awesome.com","Backend service operational and responsive"],"input_types":["Natural language queries, section identifiers"],"output_types":["Indexed sections with metadata, items with descriptions and URLs"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["MCP client integration (Claude, Cursor, VS Code with MCP support)","Network connectivity to api.context-awesome.com or local MCP server instance","Node.js 18+ if running local server","Valid section identifier from find_awesome_section results","MCP client with tool-calling support","Network connectivity to backend API or local MCP server","Node.js 18+","TypeScript compiler or pre-built JavaScript distribution","MCP-compatible client (Claude, Cursor, VS Code with MCP extension)","For HTTP/SSE: network connectivity and port availability"],"failure_modes":["Search quality depends on backend API indexing freshness — awesome lists may lag behind GitHub updates","Confidence scores are backend-computed; no transparency into scoring algorithm from client side","Natural language queries may return false positives if query terms are ambiguous or overloaded across multiple domains","Pagination state is stateless — agents must track page tokens or offsets themselves; no server-side session management","Item metadata richness depends on awesome list source quality — some lists have minimal descriptions","Large sections (1000+ items) require multiple API calls, adding latency for comprehensive retrieval","MCP is a relatively new protocol — client support varies; not all AI tools support MCP yet","Transport abstraction adds ~50-100ms overhead per request due to serialization/deserialization","Stdio transport is single-threaded and blocking; not suitable for high-concurrency scenarios","HTTP transport requires explicit port configuration and firewall rules in networked environments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:03.036Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=context-awesome","compare_url":"https://unfragile.ai/compare?artifact=context-awesome"}},"signature":"s8vMhUMsKHvc2efPtiMUFE5Kapift2Wn81WHr+fCeHFLkUMLZ90709bsQJL45MM8xBqKEqF4KFGdcJAgOmPlDg==","signedAt":"2026-06-21T23:45:07.728Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/context-awesome","artifact":"https://unfragile.ai/context-awesome","verify":"https://unfragile.ai/api/v1/verify?slug=context-awesome","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"}}