{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-bigsweetpotatostudio-hyperchat","slug":"mcp-bigsweetpotatostudio-hyperchat","name":"HyperChat","type":"repo","url":"https://github.com/BigSweetPotatoStudio/HyperChat","page_url":"https://unfragile.ai/mcp-bigsweetpotatostudio-hyperchat","categories":["chatbots-assistants"],"tags":["agent","chat-application","llm","local-agent","mcp","modelcontextprotocol"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_0","uri":"capability://automation.workflow.yaml.driven.agent.configuration.with.version.control.integration","name":"yaml-driven agent configuration with version control integration","description":"HyperChat treats AI agents as code artifacts defined through YAML configuration files that are version-controlled alongside project code in Git repositories. The system parses workspace-scoped agent definitions, manages agent lifecycle through a dedicated Agent Manager, and enables agents to maintain project-contextual memory and tool bindings. This 'AI as Code' philosophy allows agents to be portable, reproducible, and integrated into standard development workflows without cloud dependencies.","intents":["Define AI agent behavior as declarative configuration that can be committed to version control","Create project-specific agents that understand and remember local codebase context","Share agent configurations across team members through Git repositories","Version-control AI capabilities alongside application code for reproducibility"],"best_for":["Development teams building local-first AI workflows","Organizations requiring data sovereignty and no cloud AI dependencies","Projects where AI behavior must be auditable and version-tracked"],"limitations":["YAML schema validation is limited to built-in agent types; custom agent types require code changes","Agent configuration changes require manual workspace reload; no hot-reload for agent definitions","No built-in conflict resolution for concurrent agent configuration edits in collaborative scenarios"],"requires":["TypeScript/Node.js 18+","Git repository for workspace storage","YAML-compatible text editor","At least one configured AI provider (OpenAI, Anthropic, Ollama, etc.)"],"input_types":["YAML configuration files","Agent metadata (name, description, system prompt)","Tool/MCP bindings specification"],"output_types":["Agent instance with lifecycle state","Agent command execution results","Agent memory and context state"],"categories":["automation-workflow","configuration-as-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_1","uri":"capability://tool.use.integration.dual.cli.web.interface.with.shared.backend.services","name":"dual cli/web interface with shared backend services","description":"HyperChat implements a monorepo architecture with separate CLI and Web frontends that both consume the same core backend services (Agent Manager, MCP Manager, AI Channel). The CLI interface prioritizes agent-centric rapid interactions without workspace setup overhead, while the Web interface (built with React/Electron) provides multi-workspace management, collaborative features, and visual workspace configuration. Both interfaces share the same underlying service layer through a clean dependency hierarchy (shared types → core services → UI packages).","intents":["Use AI agents from the command line for rapid prototyping and scripting workflows","Manage multiple workspaces and projects through a visual web interface","Switch between CLI and Web interfaces while maintaining consistent agent state and configuration","Deploy HyperChat as both a desktop application (Electron) and web service"],"best_for":["Developers who prefer CLI-first workflows for quick AI interactions","Teams managing multiple concurrent projects requiring workspace isolation","Organizations deploying HyperChat across desktop and web environments"],"limitations":["CLI and Web interfaces cannot simultaneously manage the same workspace; requires explicit mode switching","Real-time synchronization between CLI and Web state is eventual-consistent, not immediate","Web interface requires Node.js backend server; cannot run purely client-side","Electron desktop app adds ~150MB binary size overhead vs CLI-only deployment"],"requires":["Node.js 18+","For CLI: npm/yarn package manager","For Web: React 18+, Express.js or similar backend","For Desktop: Electron 20+ runtime"],"input_types":["CLI commands and arguments","Web form inputs and workspace configuration","Chat messages and agent commands"],"output_types":["CLI text output and streaming responses","Web UI rendered components","JSON API responses for programmatic access"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_10","uri":"capability://automation.workflow.monorepo.build.orchestration.with.sequential.package.dependency.resolution","name":"monorepo build orchestration with sequential package dependency resolution","description":"HyperChat uses a TypeScript monorepo structure with npm workspaces, implementing a sequential build process where packages build in dependency order: shared types → core services → UI packages (Web, Electron, CLI). The build system uses npm scripts orchestrated through package.json, with development mode supporting concurrent package development and hot reloading. The dependency hierarchy ensures clean separation of concerns with shared types as the foundation, preventing circular dependencies.","intents":["Manage multiple related packages (core, web, electron, cli) in a single repository","Ensure consistent types and interfaces across CLI, Web, and Electron interfaces","Build and deploy different package combinations (CLI-only, Web-only, full desktop app)","Enable concurrent development of multiple packages with hot reloading"],"best_for":["Teams building multiple related tools that share common backend logic","Projects requiring consistent types across multiple interfaces","Organizations deploying different package combinations to different environments"],"limitations":["Monorepo build adds complexity; requires understanding of package dependencies","Sequential build process is slower than parallel builds; full build takes ~30-60 seconds","Circular dependency detection is not automated; requires manual verification","Shared package changes require rebuilding all dependent packages; no incremental builds"],"requires":["Node.js 18+","npm 8+ with workspace support","TypeScript 4.5+ for type checking","Build tools (esbuild, webpack, or similar)"],"input_types":["TypeScript source files","Package.json configuration","Build scripts"],"output_types":["Compiled JavaScript bundles","Type definition files (.d.ts)","Executable binaries (CLI, Electron app)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_11","uri":"capability://automation.workflow.docker.containerization.and.ci.cd.pipeline.integration","name":"docker containerization and ci/cd pipeline integration","description":"HyperChat implements Docker support for containerized deployment, with Dockerfile configurations for building container images that include Node.js runtime, dependencies, and the compiled application. The system includes CI/CD pipeline definitions (likely GitHub Actions or similar) that automate building, testing, and deploying containers. Container deployment enables HyperChat to run in Kubernetes, Docker Compose, or cloud platforms without requiring local Node.js installation.","intents":["Deploy HyperChat in containerized environments (Docker, Kubernetes, cloud platforms)","Automate building and testing through CI/CD pipelines","Run HyperChat in isolated environments without host system dependencies","Scale HyperChat across multiple container instances"],"best_for":["Teams deploying HyperChat to cloud platforms (AWS, GCP, Azure)","Organizations using Kubernetes for container orchestration","Projects requiring automated testing and deployment pipelines"],"limitations":["Container image size is large (~500MB+) due to Node.js runtime and dependencies","File-based workspace storage does not scale across multiple container instances; requires shared volume","Container networking adds latency for MCP tool access; local tools are slower in containers","Persistent storage configuration is complex; requires external volume management"],"requires":["Docker 20.10+ installed","Docker registry for storing container images","Container orchestration platform (Docker Compose, Kubernetes, etc.)","Persistent volume storage for workspace data"],"input_types":["Dockerfile configuration","CI/CD pipeline definitions (YAML)","Environment variables for container configuration"],"output_types":["Docker container images","Container logs and metrics","Deployment status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_12","uri":"capability://text.generation.language.internationalization.i18n.with.multi.language.ui.support","name":"internationalization (i18n) with multi-language ui support","description":"HyperChat implements internationalization support enabling the Web UI to be rendered in multiple languages through a translation system. The system uses language-specific resource files (likely JSON or similar) that map UI strings to translated text, with language selection in the Web interface. The CLI and core services may have limited i18n support, with primary focus on Web UI localization.","intents":["Provide HyperChat Web UI in multiple languages for global users","Allow users to select their preferred language in the Web interface","Enable community contributions of translations for new languages"],"best_for":["Teams deploying HyperChat to international audiences","Organizations requiring multi-language support for compliance or user experience"],"limitations":["i18n support is limited to Web UI; CLI and core services have minimal translation","Translation maintenance requires manual updates for each language","No automatic translation; all translations must be manually provided","Right-to-left (RTL) language support may require additional CSS/layout work"],"requires":["Translation resource files for each supported language","i18n library (likely i18next or similar)","Language selection mechanism in Web UI"],"input_types":["Translation resource files (JSON, YAML, etc.)","Language selection from user"],"output_types":["Localized UI strings","Language-specific formatting (dates, numbers, etc.)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_2","uri":"capability://text.generation.language.multi.provider.ai.model.integration.with.streaming.chat.interface","name":"multi-provider ai model integration with streaming chat interface","description":"HyperChat abstracts multiple LLM providers (OpenAI, Anthropic, Ollama, and others) through a unified AI Channel system that handles provider-agnostic chat streaming, token counting, and model selection. The system uses a provider configuration layer that maps API credentials to model endpoints, implements streaming response handling through Node.js streams, and maintains conversation history with context windowing. Chat messages flow through the AI Channel which normalizes provider-specific response formats into a common interface.","intents":["Switch between different LLM providers (OpenAI, Anthropic, Ollama) without changing agent code","Stream AI responses in real-time to CLI and Web interfaces","Maintain conversation context across multiple turns with automatic token counting","Configure multiple model endpoints for fallback or load-balancing scenarios"],"best_for":["Teams evaluating multiple LLM providers and wanting to avoid vendor lock-in","Developers building local-first applications using Ollama or other self-hosted models","Projects requiring real-time streaming responses for better UX"],"limitations":["Provider abstraction adds ~50-100ms latency per request due to normalization layer","Token counting is approximate for non-OpenAI providers; exact counts require provider-specific APIs","Streaming responses cannot be easily interrupted mid-generation in Web UI without WebSocket upgrades","No built-in provider failover; requires manual configuration for multi-provider redundancy"],"requires":["API keys for at least one provider (OpenAI, Anthropic, etc.) OR local Ollama instance","Environment variables or config file with provider credentials","Network connectivity for cloud providers; local network for Ollama","TypeScript/Node.js 18+ for backend"],"input_types":["Chat messages (text)","Provider configuration (API keys, model names, endpoints)","System prompts and conversation history"],"output_types":["Streaming text responses","Structured chat completion objects","Token usage metadata"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_3","uri":"capability://tool.use.integration.mcp.model.context.protocol.tool.integration.with.http.gateway","name":"mcp (model context protocol) tool integration with http gateway","description":"HyperChat implements the Model Context Protocol (MCP) standard to enable AI agents to invoke external tools and access local resources through a managed client lifecycle system. The MCP Manager instantiates and manages MCP client connections, the MCP Gateway exposes MCP tools via HTTP API for remote access, and agents can bind specific tools through workspace configuration. Tools are discovered through MCP server introspection, validated against schemas, and executed with automatic error handling and response streaming.","intents":["Grant AI agents access to local file systems, databases, and development tools via MCP protocol","Expose MCP tools as HTTP endpoints for integration with external systems","Discover available tools from MCP servers and bind them to specific agents","Execute tool calls with automatic schema validation and error handling"],"best_for":["Teams building AI agents that need access to local development tools and file systems","Organizations deploying MCP servers and wanting to expose them via HTTP","Projects requiring structured tool calling with schema validation"],"limitations":["MCP client lifecycle management adds ~200-500ms overhead per tool invocation due to connection setup","HTTP gateway introduces network latency; local MCP clients are significantly faster","Tool schema validation is strict; tools with dynamic or undocumented parameters may fail","No built-in tool caching; repeated tool calls with identical parameters require re-execution"],"requires":["MCP server running and accessible (local or remote)","MCP server configuration in workspace settings","Tool schema definitions from MCP server introspection","Node.js 18+ for MCP client implementation"],"input_types":["MCP server connection parameters","Tool invocation requests with parameters","Tool schema definitions (JSON Schema format)"],"output_types":["Tool execution results","Structured tool responses","Error messages with diagnostic information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_4","uri":"capability://automation.workflow.workspace.scoped.agent.and.tool.management.with.context.isolation","name":"workspace-scoped agent and tool management with context isolation","description":"HyperChat implements a Workspace Manager that provides project-level isolation for agents, tools, and configurations through a hierarchical directory structure. Each workspace maintains its own agent definitions, MCP tool bindings, settings, and conversation history in a dedicated folder. The system supports multiple concurrent workspaces with independent AI provider configurations, enabling teams to manage different projects with different tool sets and agent behaviors without cross-contamination.","intents":["Organize multiple projects as isolated workspaces with separate agents and tool configurations","Prevent tool and agent configurations from one project affecting another","Share workspace configurations across team members through Git","Maintain separate conversation histories and agent memory per workspace"],"best_for":["Teams managing multiple concurrent projects with different AI requirements","Organizations requiring strict project isolation and configuration management","Developers who want to version-control workspace configurations per project"],"limitations":["Workspace switching requires explicit selection; no automatic context detection","Cross-workspace agent communication is not supported; agents are isolated to their workspace","Workspace configuration conflicts (e.g., duplicate agent names) are not automatically resolved","Large workspaces with many agents/tools may experience slower initialization times (>2s)"],"requires":["File system with read/write permissions for workspace directories","YAML configuration files for workspace setup","At least one workspace initialized before agent creation"],"input_types":["Workspace directory path","Workspace configuration files (YAML)","Agent and tool definitions"],"output_types":["Workspace instance with loaded configuration","List of available agents and tools in workspace","Workspace-scoped conversation history"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_5","uri":"capability://planning.reasoning.agent.command.execution.with.memory.and.context.persistence","name":"agent command execution with memory and context persistence","description":"HyperChat's Agent System implements a command-based execution model where agents process user commands through a structured pipeline: command parsing, context loading (including workspace history and agent memory), LLM invocation, tool calling, and response streaming. Agent memory is persisted to disk using a conversation history store, enabling agents to maintain context across sessions. The system supports both synchronous command execution (CLI) and asynchronous streaming (Web), with automatic memory truncation for context window management.","intents":["Execute agent commands with full access to workspace context and conversation history","Maintain agent memory across multiple sessions without re-providing context","Stream agent responses in real-time while preserving conversation history","Automatically manage context window by truncating old conversation history"],"best_for":["Building stateful AI agents that remember previous interactions within a project","Creating agents that can reference and learn from workspace history","Implementing long-running agent workflows that span multiple user sessions"],"limitations":["Memory persistence is file-based; no distributed memory store for multi-instance deployments","Context window management uses simple truncation; no semantic summarization of old context","Agent memory is workspace-scoped; cannot share memory across workspaces","Memory loading adds ~100-300ms latency per command due to file I/O and history parsing"],"requires":["Workspace with initialized agent configuration","File system storage for conversation history","At least one AI provider configured","Node.js 18+ with file system access"],"input_types":["User command text","Agent configuration (system prompt, tools, model)","Conversation history from previous sessions"],"output_types":["Agent response text","Tool invocation results","Updated conversation history","Agent memory state"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_6","uri":"capability://text.generation.language.cli.agent.first.rapid.interaction.mode.with.streaming.output","name":"cli agent-first rapid interaction mode with streaming output","description":"HyperChat's CLI interface prioritizes rapid agent interaction without workspace setup overhead, implementing a command-line parser that maps user input to agent commands, streams responses in real-time using Node.js streams, and provides minimal configuration requirements. The CLI supports both interactive mode (REPL-like conversation) and command mode (single-shot execution), with automatic workspace detection and agent selection. Streaming output is rendered progressively to the terminal, enabling users to see agent responses as they generate.","intents":["Quickly invoke AI agents from the command line for scripting and automation","Stream agent responses to terminal in real-time without waiting for completion","Use HyperChat in shell scripts and CI/CD pipelines","Interact with agents without opening a web browser or GUI"],"best_for":["Developers and DevOps engineers who prefer command-line interfaces","Automation and scripting workflows where GUI is not practical","CI/CD pipelines and server environments without graphical displays"],"limitations":["CLI interface does not support rich formatting (tables, images); output is plain text only","Interactive mode requires terminal with TTY support; cannot run in non-interactive environments without modifications","Streaming output cannot be easily captured and parsed by downstream tools without additional formatting","No built-in pagination or output buffering; large responses may scroll off terminal"],"requires":["Terminal/shell environment (bash, zsh, PowerShell, etc.)","Node.js 18+ installed and in PATH","HyperChat CLI package installed globally or via npx","At least one workspace and agent configured"],"input_types":["CLI command arguments","Interactive terminal input","Environment variables for configuration"],"output_types":["Streaming text output to stdout","Error messages to stderr","Exit codes for script integration"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_7","uri":"capability://automation.workflow.web.based.workspace.management.and.multi.project.collaboration.interface","name":"web-based workspace management and multi-project collaboration interface","description":"HyperChat's Web interface (built with React and Electron for desktop) provides a visual workspace management system where users can create/edit workspaces, configure agents, manage MCP tool bindings, and view conversation history through a dashboard. The interface implements a project-centric navigation model with workspace switcher, agent list, and chat panel. The backend serves the Web UI through Express.js or similar, providing REST/WebSocket APIs for real-time updates and workspace synchronization.","intents":["Visually manage multiple workspaces and projects through a dashboard interface","Configure agents and tools without editing YAML files directly","View and search conversation history across multiple agents","Collaborate on workspace configurations with team members"],"best_for":["Non-technical users who prefer visual interfaces over YAML configuration","Teams managing multiple projects requiring visual workspace organization","Organizations deploying HyperChat as a desktop application (Electron)"],"limitations":["Web interface requires backend server; cannot run purely client-side","Real-time collaboration requires WebSocket support; REST-only deployments have eventual consistency","Electron desktop app adds significant binary size (~150MB) and memory overhead","Web UI rendering adds ~200-500ms latency compared to CLI for simple operations"],"requires":["Node.js 18+ backend server","React 18+ for frontend","Modern web browser (Chrome, Firefox, Safari, Edge)","For desktop: Electron 20+ runtime","Network connectivity between frontend and backend"],"input_types":["Form inputs for workspace/agent configuration","Chat messages in web UI","File uploads for workspace import/export"],"output_types":["Rendered React components","JSON API responses","WebSocket events for real-time updates"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_8","uri":"capability://automation.workflow.settings.and.environment.configuration.management.with.provider.abstraction","name":"settings and environment configuration management with provider abstraction","description":"HyperChat implements a Settings Manager that abstracts configuration across CLI environment variables, workspace YAML files, and application settings files. The system supports provider-specific configuration (API keys, model names, endpoints) with environment variable interpolation, enabling users to configure multiple LLM providers without hardcoding credentials. Settings are loaded in a hierarchical order (defaults → workspace config → environment variables), with environment variables taking precedence for security.","intents":["Configure multiple LLM providers (OpenAI, Anthropic, Ollama) without hardcoding API keys","Use environment variables for sensitive credentials in CI/CD and deployment scenarios","Override workspace settings with environment variables for different deployment environments","Store non-sensitive configuration in version-controlled YAML files"],"best_for":["Teams deploying HyperChat across multiple environments (dev, staging, prod)","CI/CD pipelines requiring environment-specific configuration","Organizations with strict credential management policies"],"limitations":["Settings validation is minimal; invalid configuration may only be detected at runtime","No built-in secrets management; relies on environment variables or external secret stores","Settings changes require application restart; no hot-reload for configuration updates","No audit logging for configuration changes; difficult to track who changed what settings"],"requires":["Environment variables set in shell or CI/CD platform","YAML configuration files in workspace directory","Node.js 18+ for environment variable access","File system permissions for reading configuration files"],"input_types":["Environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","YAML configuration files","Application settings UI (Web interface)"],"output_types":["Resolved configuration object","Provider-specific settings","Validation errors"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bigsweetpotatostudio-hyperchat__cap_9","uri":"capability://memory.knowledge.conversation.history.storage.and.retrieval.with.context.windowing","name":"conversation history storage and retrieval with context windowing","description":"HyperChat implements a file-based conversation history store that persists chat messages to disk in a structured format (likely JSON or similar), enabling agents to load and reference previous conversations. The system implements context windowing by truncating old messages when conversation history exceeds the LLM's token limit, using a simple FIFO (first-in-first-out) strategy. Conversation history is workspace-scoped and agent-scoped, allowing different agents to maintain separate conversation threads.","intents":["Persist conversation history to disk for long-term reference and audit trails","Load conversation history on agent startup to provide context for new commands","Automatically truncate old messages to fit within LLM context window limits","Search and retrieve specific conversations from history"],"best_for":["Building agents that need to reference previous interactions within a project","Creating audit trails of agent interactions for compliance or debugging","Implementing long-running agents that span multiple user sessions"],"limitations":["File-based storage does not scale to millions of messages; no database backend","Context windowing uses simple truncation; no semantic summarization of old context","No full-text search over conversation history; requires loading entire history into memory","Conversation history is not encrypted at rest; requires file system security for sensitive data"],"requires":["File system with read/write permissions","Workspace directory for storing conversation history","Sufficient disk space for conversation storage","Node.js 18+ for file I/O"],"input_types":["Chat messages (text)","Agent metadata (agent ID, workspace ID)","Timestamp information"],"output_types":["Conversation history array","Truncated history for context window","Search results"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["TypeScript/Node.js 18+","Git repository for workspace storage","YAML-compatible text editor","At least one configured AI provider (OpenAI, Anthropic, Ollama, etc.)","Node.js 18+","For CLI: npm/yarn package manager","For Web: React 18+, Express.js or similar backend","For Desktop: Electron 20+ runtime","npm 8+ with workspace support","TypeScript 4.5+ for type checking"],"failure_modes":["YAML schema validation is limited to built-in agent types; custom agent types require code changes","Agent configuration changes require manual workspace reload; no hot-reload for agent definitions","No built-in conflict resolution for concurrent agent configuration edits in collaborative scenarios","CLI and Web interfaces cannot simultaneously manage the same workspace; requires explicit mode switching","Real-time synchronization between CLI and Web state is eventual-consistent, not immediate","Web interface requires Node.js backend server; cannot run purely client-side","Electron desktop app adds ~150MB binary size overhead vs CLI-only deployment","Monorepo build adds complexity; requires understanding of package dependencies","Sequential build process is slower than parallel builds; full build takes ~30-60 seconds","Circular dependency detection is not automated; requires manual verification","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3892639211503924,"quality":0.5,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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":"2025-08-18T06:52:05Z"},"community":{"stars":712,"forks":74,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-bigsweetpotatostudio-hyperchat","compare_url":"https://unfragile.ai/compare?artifact=mcp-bigsweetpotatostudio-hyperchat"}},"signature":"X7t5/56Zj26Ekyr8rqWGoo7uOgIAvnmJ0J4GuN7Hd9b7wOmSSqhf0RqicFQ1/sDRQoWAonI/eh8mkOjLt4qRCw==","signedAt":"2026-06-20T10:44:34.289Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-bigsweetpotatostudio-hyperchat","artifact":"https://unfragile.ai/mcp-bigsweetpotatostudio-hyperchat","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-bigsweetpotatostudio-hyperchat","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"}}