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The framework uses a type inference system that propagates tool schemas from server registration through to React hooks on the client, enabling compile-time type checking for tool invocations without runtime type assertions. This is achieved through TypeScript generics and a manifest system that maps tool definitions to their implementations.","intents":["I want to register MCP tools with full TypeScript type safety so my IDE catches tool invocation errors at compile time","I need my ChatGPT widget to know the exact shape of tool parameters and return types without manual type definitions","I want to avoid runtime errors from mismatched tool schemas between server and client"],"best_for":["TypeScript developers building ChatGPT Apps with MCP servers","teams requiring compile-time safety for tool-based integrations","developers migrating from untyped MCP implementations"],"limitations":["TypeScript only — no Python or JavaScript support for type inference","Type inference requires explicit generic parameters on McpServer class instantiation","Complex union types in tool parameters may require manual type hints to resolve correctly"],"requires":["TypeScript 4.7+","@modelcontextprotocol/sdk installed","Node.js 18+"],"input_types":["TypeScript type definitions","Tool schema objects with JSON Schema format"],"output_types":["Typed tool registry","manifest.json with tool metadata","TypeScript type exports for client-side hooks"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpic-ai-skybridge__cap_1","uri":"capability://tool.use.integration.react.hooks.abstraction.over.chatgpt.s.window.openai.imperative.api","name":"react hooks abstraction over chatgpt's window.openai imperative api","description":"Wraps ChatGPT's injected window.openai global object with declarative React hooks (useToolInfo, useCallTool, useWidgetState, useOpenAiGlobal) that abstract away imperative callback patterns and state management. 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The system supports both development and production modes, with development mode allowing hot-reload of template changes and production mode bundling templates into optimized assets.","intents":["I want to generate widget HTML dynamically based on tool schemas without hardcoding UI structure","I need to render different widget layouts depending on the tool being invoked","I want to iterate on widget templates quickly during development with hot-reload"],"best_for":["developers building template-driven ChatGPT widgets","teams with non-React template preferences","rapid prototyping scenarios requiring quick UI iteration"],"limitations":["Handlebars syntax is limited compared to full React — complex conditional logic requires helper functions","No built-in component composition — templates are rendered as static HTML strings","Template context must be manually constructed from tool definitions — no automatic schema-to-template mapping","Hot-reload only works in development mode with DevTools running"],"requires":["Handlebars 4.7+","skybridge/server package","valid Handlebars template files in src/widgets/"],"input_types":["Handlebars template strings","Tool schema objects","Context data (JSON-serializable)"],"output_types":["HTML strings","Rendered widget markup"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpic-ai-skybridge__cap_3","uri":"capability://automation.workflow.automatic.widget.discovery.and.bundling.via.vite.plugin","name":"automatic widget discovery and bundling via vite plugin","description":"A custom Vite plugin scans the src/widgets/ directory to discover widget components, bundles each widget as an independent asset, and generates a manifest.json file mapping widget source files to their bundled outputs. The plugin handles both development and production modes: in development, it enables Hot Module Replacement (HMR) for rapid iteration; in production, it optimizes widget bundles for size and performance. The manifest enables the MCP server to locate and serve widget assets dynamically at runtime.","intents":["I want my widgets to be automatically discovered and bundled without manual configuration","I need fast iteration during development with hot-reload of widget changes","I want production widget bundles to be optimized for size and load time","I need a mapping between widget source files and their bundled outputs for runtime serving"],"best_for":["developers using Vite as their build tool","teams with multiple widgets requiring automated bundling","projects needing rapid development iteration with HMR"],"limitations":["Vite 4.0+ required — not compatible with Webpack or other bundlers","Widget discovery is filesystem-based — requires strict src/widgets/ directory structure","HMR only works with Skybridge DevTools running — standalone Vite dev server won't trigger widget reloads","Manifest generation requires write access to output directory"],"requires":["Vite 4.0+","Node.js 18+","src/widgets/ directory with widget components","Skybridge DevTools for HMR in development"],"input_types":["Widget component files (React/TypeScript)","Vite configuration"],"output_types":["Bundled widget JavaScript/CSS assets","manifest.json with widget metadata","HMR event stream (development only)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpic-ai-skybridge__cap_4","uri":"capability://automation.workflow.local.development.environment.with.integrated.devtools.ui","name":"local development environment with integrated devtools ui","description":"Skybridge provides a DevTools application that runs locally during development, offering a web-based UI for testing widgets without deploying to ChatGPT. The DevTools includes a tool panel for selecting and invoking MCP tools, a widget renderer that displays the selected widget's UI, and a development server that serves widget assets with HMR enabled. The DevTools communicates with the MCP server via stdio or HTTP, allowing developers to test tool invocations and widget interactions in an isolated environment before deploying to production.","intents":["I want to test my widgets locally without deploying to ChatGPT","I need to debug tool invocations and widget state without production constraints","I want to see widget changes instantly as I edit code (hot-reload)","I need a UI to select and invoke tools to test widget behavior"],"best_for":["developers building ChatGPT Apps locally","teams requiring rapid iteration and debugging","developers unfamiliar with ChatGPT's widget environment"],"limitations":["DevTools UI is web-based and requires a modern browser — no CLI-only testing","DevTools only simulates ChatGPT's widget environment — some window.openai APIs may behave differently in production","HMR requires the DevTools server to be running — disconnection will pause hot-reload","DevTools cannot test multi-widget interactions or cross-widget state sharing"],"requires":["Node.js 18+","Skybridge DevTools package (included in main package)","MCP server running locally","Modern browser (Chrome, Firefox, Safari, Edge)"],"input_types":["MCP server configuration","Widget source files","Tool definitions"],"output_types":["DevTools web UI","Widget render output","Tool invocation logs","HMR event stream"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpic-ai-skybridge__cap_5","uri":"capability://code.generation.editing.typed.helper.factory.for.generating.widget.specific.utility.functions","name":"typed helper factory for generating widget-specific utility functions","description":"The generateHelpers<AppType>() factory function creates a set of typed utility functions scoped to a specific widget's tool context. These helpers provide type-safe wrappers around useCallTool and other hooks, with pre-bound tool names and parameter types inferred from the AppType generic. This eliminates the need to manually specify tool names and types in every hook call, reducing boilerplate and improving IDE autocomplete for tool invocations within a widget.","intents":["I want to create typed helper functions for my widget's specific tools without manual type annotations","I need IDE autocomplete for tool names and parameters within my widget","I want to reduce boilerplate when calling the same tools repeatedly in a widget"],"best_for":["TypeScript developers building widgets with multiple tool calls","teams using IDE autocomplete heavily","developers wanting to minimize type annotations in widget code"],"limitations":["Requires explicit AppType generic parameter — cannot infer from context","Helper functions are generated at runtime — no static code generation","Helpers are scoped to a single widget — cross-widget helper sharing requires manual composition"],"requires":["TypeScript 4.7+","skybridge/web package","AppType generic with tool definitions"],"input_types":["AppType generic parameter (TypeScript type)"],"output_types":["Typed helper functions","Tool invocation wrappers"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpic-ai-skybridge__cap_6","uri":"capability://automation.workflow.widget.lifecycle.management.with.development.vs.production.modes","name":"widget lifecycle management with development vs production modes","description":"Skybridge manages widget initialization, mounting, and cleanup differently in development and production environments. In development mode, widgets are loaded from the local DevTools server with HMR enabled, allowing instant updates without page reload. In production mode, widgets are served from bundled assets with optimized loading and error handling. The framework handles iframe creation, context injection (window.openai API, environment variables), and cleanup on widget unmount, abstracting away the complexity of ChatGPT's widget lifecycle.","intents":["I want my widget to load correctly in both development and production without code changes","I need environment-specific configuration (API keys, endpoints) injected into my widget","I want proper cleanup when widgets are unmounted to avoid memory leaks","I need HMR to work seamlessly during development without manual page reloads"],"best_for":["developers building widgets that need to work in multiple environments","teams with strict production reliability requirements","developers needing environment-specific configuration"],"limitations":["Development and production modes have different asset loading paths — testing production mode locally requires a build step","Environment variables must be explicitly listed in configuration — no automatic detection","Widget cleanup is not guaranteed if the iframe is forcefully destroyed — relies on React cleanup hooks","HMR state is not persisted across reloads — widget state resets on hot-reload"],"requires":["Skybridge DevTools (development mode)","Built widget assets (production mode)","Environment configuration file"],"input_types":["Environment variables","Widget source files","Configuration objects"],"output_types":["Initialized widget instances","Injected context (window.openai, env vars)","HMR event stream (development)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpic-ai-skybridge__cap_7","uri":"capability://text.generation.language.data.llm.attribute.based.widget.targeting.and.context.passing","name":"data-llm attribute-based widget targeting and context passing","description":"Skybridge uses a data-llm HTML attribute system to mark elements within widgets that should receive LLM context or trigger specific behaviors. This attribute enables declarative specification of which widget elements correspond to tool parameters, outputs, or state, allowing the framework to automatically pass context to the LLM without explicit JavaScript code. The system integrates with the widget rendering pipeline to inject data-llm attributes into template-rendered HTML, enabling semantic markup that ChatGPT can interpret for better context understanding.","intents":["I want to mark widget elements that should be visible to the LLM for context","I need to specify which widget outputs correspond to tool results without JavaScript","I want to enable ChatGPT to understand widget structure and state through semantic markup"],"best_for":["developers using Handlebars templates for widgets","teams wanting to improve LLM context understanding of widget state","projects requiring semantic markup for ChatGPT integration"],"limitations":["data-llm attribute system is not well-documented — requires understanding ChatGPT's context parsing","Attribute values must follow ChatGPT's expected format — no validation or type checking","data-llm attributes are only effective if ChatGPT's widget environment recognizes them — behavior may vary across ChatGPT versions","No built-in way to test whether data-llm attributes are being parsed correctly"],"requires":["Handlebars templates with data-llm attributes","ChatGPT widget environment that recognizes data-llm attributes"],"input_types":["HTML template strings","data-llm attribute values"],"output_types":["Rendered HTML with data-llm attributes","LLM context markup"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpic-ai-skybridge__cap_8","uri":"capability://tool.use.integration.mcp.server.stdio.http.transport.abstraction.with.chatgpt.integration","name":"mcp server stdio/http transport abstraction with chatgpt integration","description":"Skybridge extends the official MCP SDK's transport layer to support both stdio and HTTP communication protocols, with built-in support for ChatGPT's widget environment. The McpServer class handles transport initialization, message routing, and error handling transparently, allowing developers to focus on tool and widget registration without managing low-level protocol details. The framework automatically configures the appropriate transport based on the deployment context (local development vs ChatGPT production).","intents":["I want to run my MCP server with stdio transport for local development without configuration","I need my MCP server to work with ChatGPT's HTTP-based widget environment","I want automatic transport selection based on the deployment context"],"best_for":["developers building MCP servers for ChatGPT","teams needing both local and cloud deployment","developers unfamiliar with MCP transport protocols"],"limitations":["Transport selection is automatic — no manual override mechanism for testing different transports","HTTP transport requires CORS configuration — may not work with all ChatGPT deployment scenarios","Error handling is abstracted — debugging transport-level issues requires accessing raw MCP logs","No built-in support for WebSocket transport — only stdio and HTTP"],"requires":["@modelcontextprotocol/sdk","Node.js 18+","Valid MCP server implementation"],"input_types":["MCP server configuration","Tool definitions","Transport parameters"],"output_types":["MCP protocol messages","Tool invocation results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpic-ai-skybridge__cap_9","uri":"capability://memory.knowledge.state.management.hooks.for.widget.scoped.persistence","name":"state management hooks for widget-scoped persistence","description":"Skybridge provides useWidgetState hook that manages persistent state within a widget's scope, automatically syncing state across tool invocations and widget re-renders. 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State is scoped to the widget instance, preventing cross-widget state pollution while enabling widgets to maintain context across multiple tool calls.","intents":["I want to persist widget state across tool invocations without implementing custom storage","I need state to be scoped to my widget instance without affecting other widgets","I want a React-like setState interface for managing widget state"],"best_for":["React developers building stateful ChatGPT widgets","teams needing widget-scoped state without external databases","developers familiar with React hooks patterns"],"limitations":["State is widget-scoped — no cross-widget state sharing without external store","State persistence is not guaranteed if the widget iframe is destroyed — relies on storage mechanism","Large state objects may impact performance — no built-in optimization for large datasets","No built-in state synchronization across multiple widget instances of the same tool"],"requires":["React 16.8+","ChatGPT widget environment","skybridge/web package"],"input_types":["Initial state value","State update functions"],"output_types":["Current state value","State setter function"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["TypeScript 4.7+","@modelcontextprotocol/sdk installed","Node.js 18+","React 16.8+","ChatGPT widget environment with window.openai API","skybridge/web package installed","Environment configuration file","skybridge/web package","Environment variables set in deployment environment","Valid MCP tool definitions"],"failure_modes":["TypeScript only — no Python or JavaScript support for type inference","Type inference requires explicit generic parameters on McpServer class instantiation","Complex union types in tool parameters may require manual type hints to resolve correctly","React 16.8+ required — hooks cannot be used in class components","Hooks only work within ChatGPT widget iframe context — will fail if window.openai is not injected","State persistence is scoped to widget instance — no cross-widget state sharing without external store","Async tool calls have no built-in timeout or cancellation mechanism","Environment variables must be explicitly listed in configuration — no automatic detection","Configuration is injected at widget initialization — changes require widget reload","No built-in encryption for sensitive values — relies on ChatGPT's iframe isolation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4142627559509797,"quality":0.49,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:38.364Z","last_commit":"2026-05-02T20:38:04Z"},"community":{"stars":1117,"forks":60,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-alpic-ai-skybridge","compare_url":"https://unfragile.ai/compare?artifact=mcp-alpic-ai-skybridge"}},"signature":"XbDZpPPqjk+hqbPpgTHKkBi5D6N+j186O6PCoaWdtdK8Uz1CH9bbl0hLfCRBKlK93e30iKfYJW9WHu7DmtjhDg==","signedAt":"2026-06-21T01:50:40.911Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-alpic-ai-skybridge","artifact":"https://unfragile.ai/mcp-alpic-ai-skybridge","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-alpic-ai-skybridge","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"}}