{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-interactive-mcp","slug":"interactive-mcp","name":"interactive-mcp","type":"mcp","url":"https://github.com/ttommyth/interactive-mcp","page_url":"https://unfragile.ai/interactive-mcp","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-interactive-mcp__cap_0","uri":"capability://tool.use.integration.user.input.request.with.options","name":"user-input-request-with-options","description":"Allows LLMs to pause execution and request specific information from users via a blocking MCP tool call that displays optional predefined choices and timeout support. Implements a request_user_input tool registered with the MCP server that captures user responses through terminal UI components built with React/Ink, returning the selected or typed response back to the LLM context.","intents":["Ask the LLM to request clarification or missing information from the user when it cannot proceed","Present multiple predefined options to the user and capture their selection","Implement timeout-based user input with fallback behavior","Break LLM execution flow to inject real-time user decisions into the reasoning chain"],"best_for":["LLM agent developers building interactive workflows that require user confirmation","Teams building multi-step processes where LLMs need to ask clarifying questions","Developers implementing human-in-the-loop AI systems with explicit decision points"],"limitations":["Blocks LLM execution until user responds — no asynchronous input handling","Timeout support exists but fallback behavior on timeout is not specified in architecture","Terminal-based UI only — no web or GUI alternative for input collection","Single-user interaction model — no multi-user or queued request handling"],"requires":["Node.js runtime with MCP SDK (@modelcontextprotocol/sdk)","LLM client configured to recognize and call MCP tools","Terminal/CLI environment with React/Ink rendering capability","Local OS access for terminal UI rendering"],"input_types":["JSON schema with question text, optional choices array, optional timeout milliseconds"],"output_types":["string (user-typed response or selected option)"],"categories":["tool-use-integration","human-in-the-loop"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-interactive-mcp__cap_1","uri":"capability://tool.use.integration.os.notification.dispatch","name":"os-notification-dispatch","description":"Enables LLMs to trigger native operating system notifications via the message_complete_notification MCP tool, using node-notifier for cross-platform support (Windows, macOS, Linux). The tool accepts a message string and dispatches it through the OS notification system, allowing LLMs to alert users asynchronously without blocking execution or requiring terminal focus.","intents":["Notify users when long-running LLM operations complete","Send alerts from LLM agents without interrupting user workflow","Provide completion status updates that appear in system notification centers","Implement asynchronous feedback mechanisms in background LLM tasks"],"best_for":["LLM agents running background tasks that need completion notifications","Developers building unattended AI workflows with user status updates","Teams implementing notification-driven LLM orchestration"],"limitations":["Notifications are fire-and-fire — no delivery confirmation or user interaction tracking","Limited customization of notification appearance (title, icon, sound not exposed in tool schema)","Cross-platform notification behavior varies (Windows toast, macOS banner, Linux desktop environment dependent)","No notification queue or rate limiting — rapid successive calls may overwhelm system notification center"],"requires":["Node.js runtime with node-notifier dependency","Operating system with notification daemon (Windows 10+, macOS 10.8+, Linux with D-Bus)","MCP server running with message_complete_notification tool enabled"],"input_types":["string (notification message text)"],"output_types":["void (fire-and-forget notification dispatch)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-interactive-mcp__cap_2","uri":"capability://tool.use.integration.intensive.chat.session.management","name":"intensive-chat-session-management","description":"Provides LLMs with the ability to initiate and manage persistent, multi-turn chat sessions via three coordinated MCP tools: start_intensive_chat (creates dedicated terminal interface), ask_intensive_chat (sends messages within active session), and stop_intensive_chat (closes session and cleans resources). Uses React/Ink terminal UI to render a dedicated chat interface that maintains context across multiple LLM-user exchanges within a single session lifecycle.","intents":["Conduct extended, focused dialogue sessions for complex problem-solving without returning to main LLM loop","Maintain conversation context across multiple turns within a dedicated terminal interface","Allow LLMs to engage users in iterative refinement or debugging workflows","Implement multi-turn interactions that feel like continuous conversation rather than discrete tool calls"],"best_for":["LLM agents implementing interactive debugging or code review workflows","Developers building conversational AI systems with sustained user engagement","Teams needing multi-turn user interaction within a single LLM task execution"],"limitations":["Session state is in-memory only — no persistence across server restarts","Single active session per server instance — no concurrent chat sessions","Terminal-based UI only — no web or mobile client support for intensive chat","Context from intensive chat is not automatically fed back to main LLM loop — manual message passing required","No built-in session timeout or idle cleanup — requires explicit stop_intensive_chat call"],"requires":["Node.js runtime with MCP SDK and React/Ink dependencies","Terminal environment with full TTY support for interactive UI rendering","LLM client capable of sequencing three separate tool calls (start, ask, stop)","Local OS access for terminal control"],"input_types":["start_intensive_chat: no parameters (initializes session)","ask_intensive_chat: string (message to send within active session)","stop_intensive_chat: no parameters (terminates session)"],"output_types":["start_intensive_chat: void (session initialized)","ask_intensive_chat: string (user response within session)","stop_intensive_chat: void (session closed)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-interactive-mcp__cap_3","uri":"capability://tool.use.integration.mcp.protocol.server.with.tool.registration","name":"mcp-protocol-server-with-tool-registration","description":"Implements a full Model Context Protocol (MCP) server that registers and exposes the interactive tools (user input, notifications, chat) as callable MCP tools to LLM clients. Built on the @modelcontextprotocol/sdk, the server initializes with configuration for name, version, and capabilities, then registers tool handlers that map incoming MCP tool calls to the underlying implementation (terminal UI, OS notifications, chat session management).","intents":["Expose interactive capabilities to any MCP-compatible LLM client (Claude, custom agents, etc.)","Standardize tool interface through MCP schema validation using zod","Enable LLM clients to discover available interactive tools via MCP capabilities","Provide structured request/response handling for all interactive operations"],"best_for":["Developers integrating interactive workflows into MCP-based LLM systems","Teams building LLM agents that need standardized tool interfaces","Organizations adopting MCP as their LLM integration standard"],"limitations":["MCP server runs locally only — no remote client support","Tool schema is static at startup — no dynamic tool registration or runtime modification","Error handling and tool call validation depends on client implementation","No built-in authentication or access control for tool calls","Requires LLM client to explicitly call tools — no automatic tool invocation"],"requires":["@modelcontextprotocol/sdk package","Node.js 18+ (based on package.json dependencies)","TypeScript compilation or JavaScript runtime","MCP-compatible LLM client (e.g., Claude with MCP support)"],"input_types":["MCP tool call requests with JSON arguments matching registered tool schemas"],"output_types":["MCP tool call responses with structured results or error messages"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-interactive-mcp__cap_4","uri":"capability://automation.workflow.terminal.ui.rendering.with.react.ink","name":"terminal-ui-rendering-with-react-ink","description":"Renders interactive terminal user interfaces for input prompts, option selection, and chat sessions using React and Ink (React renderer for terminal). The architecture uses React components to define UI structure (prompts with choices, chat message display, input fields) and Ink to render them to the terminal, providing a responsive, component-based approach to terminal UI that updates in real-time as user input is received.","intents":["Display rich, interactive prompts in the terminal with visual feedback","Render option lists with keyboard navigation and selection","Build a persistent chat interface that updates as messages are exchanged","Provide responsive terminal UI that doesn't block on rendering"],"best_for":["Developers building CLI tools that need rich interactive UIs","Teams implementing terminal-based LLM agent interfaces","Projects requiring cross-platform terminal UI without external dependencies"],"limitations":["Terminal-only rendering — no GUI or web UI support","Ink rendering performance degrades with very large message histories in chat sessions","Limited to terminal color and styling capabilities — no custom fonts or graphics","Requires full TTY support — does not work in non-interactive environments (CI/CD, piped input)","React component lifecycle and state management adds complexity compared to imperative terminal libraries"],"requires":["React and Ink npm packages","Node.js runtime with TTY support","Terminal emulator with ANSI color support","TypeScript or JavaScript environment"],"input_types":["React component props (prompt text, choices, chat messages)"],"output_types":["Terminal-rendered UI with ANSI escape codes"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-interactive-mcp__cap_5","uri":"capability://tool.use.integration.schema.based.tool.argument.validation","name":"schema-based-tool-argument-validation","description":"Validates all MCP tool arguments using zod schema definitions before execution, ensuring type safety and preventing invalid tool calls from reaching the implementation layer. Each tool (request_user_input, message_complete_notification, etc.) has a corresponding zod schema that defines required/optional fields, types, and constraints, which is checked at the MCP server level before the tool handler is invoked.","intents":["Prevent invalid tool calls from reaching implementation code","Provide clear error messages when LLM clients pass malformed arguments","Enforce type safety across the MCP tool interface","Enable runtime validation without manual type checking in tool handlers"],"best_for":["Teams building robust MCP servers that need input validation","Developers implementing tool interfaces with strict type requirements","Projects requiring clear error feedback for invalid tool calls"],"limitations":["Validation errors are returned to LLM client — no automatic retry or correction","Schema definitions are static at server startup — no dynamic schema updates","Complex nested schemas may produce verbose error messages","Validation adds latency to each tool call (typically <1ms but measurable at scale)"],"requires":["zod npm package","Tool schema definitions for each MCP tool","MCP server integration with zod validation"],"input_types":["JSON arguments from MCP tool calls"],"output_types":["Validated arguments (if valid) or error response (if invalid)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-interactive-mcp__cap_6","uri":"capability://automation.workflow.cli.argument.parsing.with.yargs","name":"cli-argument-parsing-with-yargs","description":"Parses command-line options for the interactive-mcp server using yargs, enabling configuration of server behavior via CLI flags (e.g., port, host, tool enable/disable). The yargs integration provides a structured way to define CLI options, parse process.argv, and pass configuration to the MCP server initialization, supporting both short and long option names with type coercion and validation.","intents":["Configure which interactive tools are enabled at server startup","Set server host and port for MCP client connections","Enable/disable specific features (notifications, chat sessions) via CLI","Provide flexible deployment configuration without code changes"],"best_for":["Developers deploying interactive-mcp in different environments","Teams needing to enable/disable tools based on deployment context","Projects requiring flexible CLI configuration for MCP servers"],"limitations":["Configuration is read-only at startup — no runtime reconfiguration","CLI options are not documented in the artifact description — requires reading source code","No configuration file support (YAML, JSON) — CLI-only configuration","No environment variable fallback for CLI options"],"requires":["yargs npm package","Node.js process.argv access","CLI invocation of the server"],"input_types":["CLI flags and arguments (e.g., --port 3000, --enable-chat)"],"output_types":["Parsed configuration object passed to MCP server"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-interactive-mcp__cap_7","uri":"capability://tool.use.integration.modular.tool.system.architecture","name":"modular-tool-system-architecture","description":"Implements a modular architecture where each interactive capability (user input, notifications, chat) is encapsulated as a separate tool with its own handler, UI component, and schema definition. The MCP server registers each tool independently, allowing tools to be enabled/disabled, tested, and maintained separately while sharing common infrastructure (MCP protocol, terminal rendering, notification dispatch).","intents":["Organize interactive capabilities into maintainable, testable modules","Enable selective tool deployment (e.g., enable chat but disable notifications)","Facilitate adding new interactive tools without modifying core server logic","Separate concerns between tool implementation, UI rendering, and MCP protocol handling"],"best_for":["Teams building extensible MCP servers with multiple tools","Developers maintaining large interactive-mcp deployments","Projects requiring selective tool enablement based on deployment context"],"limitations":["Modular design adds code complexity compared to monolithic implementation","Tool interdependencies (e.g., chat session state) require careful coordination","No plugin system for third-party tool extensions — tools must be built into the server","Shared state management between tools is not explicitly documented"],"requires":["TypeScript/JavaScript module system","MCP SDK for tool registration","Clear separation of concerns in codebase"],"input_types":["Tool-specific MCP requests"],"output_types":["Tool-specific MCP responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-interactive-mcp__cap_8","uri":"capability://planning.reasoning.bidirectional.llm.user.communication.loop","name":"bidirectional-llm-user-communication-loop","description":"Enables true bidirectional communication where LLMs can request user input, receive responses, and incorporate them back into reasoning, rather than one-way LLM-to-user communication. The architecture implements this through blocking MCP tool calls (request_user_input, ask_intensive_chat) that pause LLM execution, wait for user response, and return the response as tool output that the LLM can use in subsequent reasoning steps, creating a synchronous request-response loop.","intents":["Allow LLMs to ask clarifying questions and incorporate user answers into decision-making","Implement human-in-the-loop workflows where LLM reasoning depends on user input","Break LLM execution flow to inject real-time user decisions at critical points","Enable iterative refinement where LLM and user collaborate on problem-solving"],"best_for":["LLM agent developers building interactive workflows with user collaboration","Teams implementing human-in-the-loop AI systems with explicit decision points","Projects requiring LLM reasoning to depend on real-time user input"],"limitations":["Blocking tool calls add latency to LLM execution — each user interaction pauses the LLM","User response timeout handling is not fully specified — unclear what happens if user doesn't respond","No asynchronous input handling — LLM must wait for synchronous response","User input is not automatically validated — LLM must handle invalid responses","Context window implications of multi-turn interactions not addressed"],"requires":["LLM client that supports blocking tool calls and can wait for responses","MCP server running with user input tools enabled","Terminal environment for user interaction","Synchronous communication channel between LLM and MCP server"],"input_types":["LLM tool call requests (question, options, timeout)"],"output_types":["User response (text or selected option) returned as tool output to LLM"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"moderate","permissions":["Node.js runtime with MCP SDK (@modelcontextprotocol/sdk)","LLM client configured to recognize and call MCP tools","Terminal/CLI environment with React/Ink rendering capability","Local OS access for terminal UI rendering","Node.js runtime with node-notifier dependency","Operating system with notification daemon (Windows 10+, macOS 10.8+, Linux with D-Bus)","MCP server running with message_complete_notification tool enabled","Node.js runtime with MCP SDK and React/Ink dependencies","Terminal environment with full TTY support for interactive UI rendering","LLM client capable of sequencing three separate tool calls (start, ask, stop)"],"failure_modes":["Blocks LLM execution until user responds — no asynchronous input handling","Timeout support exists but fallback behavior on timeout is not specified in architecture","Terminal-based UI only — no web or GUI alternative for input collection","Single-user interaction model — no multi-user or queued request handling","Notifications are fire-and-fire — no delivery confirmation or user interaction tracking","Limited customization of notification appearance (title, icon, sound not exposed in tool schema)","Cross-platform notification behavior varies (Windows toast, macOS banner, Linux desktop environment dependent)","No notification queue or rate limiting — rapid successive calls may overwhelm system notification center","Session state is in-memory only — no persistence across server restarts","Single active session per server instance — no concurrent chat sessions","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":0.52,"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":"2026-06-17T09:51:03.577Z","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=interactive-mcp","compare_url":"https://unfragile.ai/compare?artifact=interactive-mcp"}},"signature":"wtKjra8grMYJ0traUVqoLB67ywcbwvchvYm3CMiy+/0etyF+ekHtMUzG1yVfl8ajRRKv7ThdId2ZoX6cvsVCCQ==","signedAt":"2026-06-20T00:18:50.899Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/interactive-mcp","artifact":"https://unfragile.ai/interactive-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=interactive-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"}}