{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-bytedance-ui-tars-desktop","slug":"mcp-bytedance-ui-tars-desktop","name":"UI-TARS-desktop","type":"repo","url":"https://github.com/bytedance/UI-TARS-desktop","page_url":"https://unfragile.ai/mcp-bytedance-ui-tars-desktop","categories":["frameworks-sdks"],"tags":["agent","agent-tars","browser-use","computer-use","cowork","gui-agent","gui-operator","mcp","mcp-server","multimodal","tars","ui-tars","vision","vlm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-bytedance-ui-tars-desktop__cap_0","uri":"capability://planning.reasoning.multimodal.agent.orchestration.with.composable.plugins","name":"multimodal-agent-orchestration-with-composable-plugins","description":"Orchestrates multimodal AI agents through a ComposableAgent plugin architecture that dynamically chains GUI, code, MCP, and browser automation tools. Implements a T5 format streaming parser for structured LLM output and a Tarko framework execution loop that manages agent state, tool invocation, and event streaming. Agents receive vision-language model outputs (screenshots, structured data) and route them through specialized plugin handlers that execute actions and feed results back into the reasoning loop.","intents":["Build a general-purpose AI agent that can browse the web, execute code, and interact with desktop UIs without hardcoding tool sequences","Compose multiple specialized agents (GUI, code, MCP) into a single orchestrated workflow that shares context and state","Stream agent reasoning and tool execution events in real-time to a frontend or external system for transparency and debugging"],"best_for":["Teams building multi-capability AI agents that need to combine browser automation, code execution, and GUI interaction","Developers integrating vision-language models with structured tool calling and streaming output parsing","Organizations deploying agents that require hot-swappable tool plugins and runtime reconfiguration"],"limitations":["Plugin architecture adds abstraction overhead — each tool invocation passes through plugin handler dispatch, adding ~50-100ms per step","T5 format parser requires strict LLM output formatting; malformed streaming responses can break parsing state","No built-in persistence for agent state across sessions — requires external storage for long-running workflows","Tarko execution loop is synchronous; concurrent tool execution not natively supported without custom plugin implementation"],"requires":["Node.js 18+ (TypeScript runtime)","OpenAI-compatible vision LLM API (Claude, GPT-4V, or local VLM endpoint)","Electron 24+ for desktop app variant","MCP server implementations for tool integration (optional but recommended)"],"input_types":["natural language instructions (text)","screenshots/images (for vision-language model input)","structured tool schemas (JSON)","code snippets for execution context"],"output_types":["event stream (JSON-formatted agent events)","tool invocation results (structured data)","execution logs and reasoning traces","final agent output (text, code, or structured data)"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_1","uri":"capability://automation.workflow.gui.automation.via.screenshot.vlm.action.loop","name":"gui-automation-via-screenshot-vlm-action-loop","description":"Automates desktop and web UI interactions by capturing screenshots, sending them to a vision-language model (VLM), parsing structured action commands (click, type, scroll), and executing them via the GUIAgent SDK. The SDK provides operator implementations for local (Electron-based) and remote (VNC/RDP) desktop control, with coordinate-based action execution and screen state feedback loops. Supports both UI-TARS proprietary models (Doubao-1.5-UI-TARS) and generic vision LLMs through a configurable VLM provider interface.","intents":["Automate repetitive desktop tasks (form filling, data entry, UI navigation) without writing brittle selectors or automation scripts","Enable AI agents to control remote desktops or web applications by reasoning about visual layout and UI elements","Build GUI testing and validation workflows that understand UI semantics rather than relying on DOM inspection or accessibility trees"],"best_for":["Teams automating legacy desktop applications or web UIs that lack API access","Organizations deploying remote desktop automation (VNC/RDP) with AI reasoning","QA and testing teams building visual regression and interaction testing workflows"],"limitations":["Screenshot-based approach adds latency — full screenshot capture, VLM inference, and action execution typically takes 2-5 seconds per step","VLM hallucination risk: models may misidentify UI elements or generate invalid coordinates, requiring error recovery logic","Coordinate-based actions are fragile across different screen resolutions and UI scaling factors","No native support for accessibility APIs (ARIA, UIA) — relies purely on visual understanding, missing semantic context"],"requires":["VLM API access (OpenAI GPT-4V, Claude, or local Doubao-1.5-UI-TARS model)","Electron 24+ for local desktop control, or VNC/RDP server for remote control","System permissions for screenshot capture and input simulation (macOS/Windows/Linux)","UI-TARS SDK (TypeScript) or compatible operator implementation"],"input_types":["screenshots (PNG/JPEG from framebuffer)","natural language task descriptions","UI element descriptions or visual context"],"output_types":["structured action commands (click, type, scroll, wait)","execution results (success/failure with error details)","updated screenshots for next iteration","task completion status"],"categories":["automation-workflow","image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_10","uri":"capability://planning.reasoning.agent.hooks.and.lifecycle.event.system","name":"agent-hooks-and-lifecycle-event-system","description":"Implements a hooks and lifecycle event system that allows custom code to execute at specific points in the agent execution loop (before/after tool call, on error, on completion). Hooks are registered at agent initialization and invoked by the Tarko framework during execution, enabling extensibility without modifying core agent code. Events include reasoning, tool_call, result, error, and completion, with detailed context passed to hook handlers.","intents":["Extend agent behavior without modifying core agent code (logging, monitoring, custom error handling)","Implement custom logic at specific execution points (e.g., validate tool calls before execution, log results to external system)","Build observability and monitoring on top of agent execution (metrics, traces, alerts)"],"best_for":["Teams building custom agent extensions and integrations","Organizations requiring detailed observability and monitoring of agent execution","Developers implementing custom error handling or validation logic"],"limitations":["Hook execution is synchronous; long-running hooks block agent execution","Hook errors can crash agent execution if not properly handled; requires defensive error handling","Hook registration is at agent initialization; dynamic hook registration requires agent restart","No built-in hook ordering or dependency management; complex hook interactions can be difficult to debug"],"requires":["JavaScript/TypeScript runtime for hook implementation","Agent initialization code to register hooks","Understanding of agent execution lifecycle and event types"],"input_types":["hook handler functions (JavaScript/TypeScript)","event context (agent state, tool details, results)"],"output_types":["hook execution results (modifications to agent state, side effects)","event propagation (continue or abort execution)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_11","uri":"capability://automation.workflow.runtime.settings.and.dynamic.agent.reconfiguration","name":"runtime-settings-and-dynamic-agent-reconfiguration","description":"Provides runtime settings management that allows agents to be reconfigured without restart, including tool registration, model parameters, execution timeouts, and resource limits. Settings are stored in a configuration object that can be updated via REST API or programmatically, with changes taking effect immediately for new tool invocations. Supports per-session and global settings with hierarchical override (session > global).","intents":["Adjust agent behavior (timeouts, resource limits, tool availability) without restarting the agent","Enable A/B testing of different agent configurations without redeployment","Provide operators with runtime control over agent execution parameters"],"best_for":["Teams running long-lived agent services that need runtime configuration updates","Organizations A/B testing different agent configurations","Operators managing agent deployments and needing runtime control"],"limitations":["In-flight tool invocations use old settings; configuration changes only affect subsequent invocations","No built-in validation of configuration changes; invalid settings can cause runtime errors","Settings are not persisted by default; server restart reverts to initial configuration","No audit trail of configuration changes; tracking who changed what requires external logging"],"requires":["REST API client for configuration updates (optional; can be programmatic)","Understanding of valid configuration options and their effects","Optional: database for configuration persistence"],"input_types":["configuration updates (JSON)","setting names and values","scope (session or global)"],"output_types":["updated configuration (JSON)","confirmation of changes","validation errors (if any)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_12","uri":"capability://planning.reasoning.agent.runner.and.loop.executor.with.streaming.output","name":"agent-runner-and-loop-executor-with-streaming-output","description":"Implements the core agent execution loop (Agent Runner) that orchestrates reasoning, tool invocation, and result feedback in an iterative cycle. The loop executor manages execution state, handles streaming output from the LLM, invokes tools via the tool call engine, and feeds results back into the next reasoning step. Supports configurable loop termination conditions (max iterations, tool completion, explicit stop) and provides detailed execution traces for debugging.","intents":["Execute agents in a structured loop that alternates between reasoning and tool invocation","Stream agent reasoning and tool results in real-time to external systems","Provide detailed execution traces for debugging and understanding agent behavior"],"best_for":["Developers building agent frameworks and execution engines","Teams requiring detailed visibility into agent execution for debugging and optimization","Organizations implementing custom agent execution strategies"],"limitations":["Loop executor is synchronous; concurrent tool execution requires custom implementation","Streaming output adds complexity; buffering and ordering of events must be carefully managed","Loop termination conditions are fixed; custom termination logic requires framework modification","Execution traces can be large for long-running agents; storage and retrieval can be expensive"],"requires":["LLM provider with streaming support","Tool call engine implementation","Agent configuration and initial state","Node.js 18+ for executor runtime"],"input_types":["agent configuration (model, tools, parameters)","initial user query or instruction","loop termination conditions"],"output_types":["streaming execution events (reasoning, tool_call, result)","final agent output","execution trace (detailed step-by-step log)","execution statistics (iterations, time, tokens)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_13","uri":"capability://tool.use.integration.tool.call.engine.with.schema.validation.and.multi.strategy.execution","name":"tool-call-engine-with-schema-validation-and-multi-strategy-execution","description":"Implements a tool call engine that validates tool invocations against registered tool schemas, handles tool execution via multiple strategies (direct function call, MCP server, subprocess), and manages tool result formatting. The engine supports tool retries on failure, timeout handling, and error recovery. Tool execution strategies are pluggable, allowing custom implementations for specific tool types (e.g., subprocess for shell commands, MCP for remote tools).","intents":["Validate tool calls before execution, catching invalid invocations early","Execute tools via multiple strategies (direct, MCP, subprocess) without changing agent code","Handle tool errors and retries transparently, improving agent robustness"],"best_for":["Developers building tool execution engines with validation and error handling","Teams integrating multiple tool types (direct functions, MCP servers, subprocesses) into agents","Organizations requiring robust tool execution with retry and timeout handling"],"limitations":["Schema validation adds overhead (~10-20ms per tool call); large numbers of tools can impact latency","Tool execution strategies are synchronous; concurrent execution requires custom implementation","Error handling is per-strategy; different tool types may have different error semantics","Tool result formatting is strategy-specific; inconsistent result types can complicate agent logic"],"requires":["Tool schema definitions (JSON schema)","Tool implementation or MCP server for each tool","Execution strategy implementations (direct, MCP, subprocess, etc.)","Node.js 18+ for engine runtime"],"input_types":["tool call request (tool name, arguments)","tool schema registry","execution strategy configuration"],"output_types":["tool execution result (structured data or text)","execution status (success, error, timeout)","error messages and retry information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_14","uri":"capability://text.generation.language.content.rendering.system.for.agent.outputs","name":"content-rendering-system-for-agent-outputs","description":"Provides a content rendering system that formats agent outputs (text, code, images, structured data) for display in the web UI or other frontends. Supports rendering of code blocks with syntax highlighting, images with metadata, structured data as tables or JSON, and markdown-formatted text. The rendering system is extensible, allowing custom renderers for specific content types.","intents":["Display agent outputs in a user-friendly format (code with syntax highlighting, formatted text, images)","Support multiple content types (text, code, images, structured data) in a unified rendering system","Enable custom rendering for domain-specific content types"],"best_for":["Teams building web UIs for agent outputs","Organizations displaying diverse content types (code, images, data) from agents","Developers implementing custom content renderers"],"limitations":["Rendering performance depends on content size; large outputs (100MB+) can cause UI lag","Custom renderers require JavaScript/React knowledge; limited to web-based rendering","No built-in optimization for large datasets; rendering tables with 10,000+ rows can be slow","Markdown rendering is basic; complex markdown features may not be supported"],"requires":["React or compatible UI framework","Content type definitions and metadata","Optional: custom renderer implementations"],"input_types":["agent output (text, code, images, structured data)","content type and metadata","rendering configuration"],"output_types":["rendered HTML/React components","formatted display in web UI"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_2","uri":"capability://tool.use.integration.mcp.server.integration.with.dynamic.tool.registry","name":"mcp-server-integration-with-dynamic-tool-registry","description":"Integrates Model Context Protocol (MCP) servers as dynamically registered tools within the agent framework, using an MCP client architecture that handles transport (stdio, SSE, WebSocket), schema discovery, and tool invocation. The MCP Agent Plugin wraps MCP server capabilities into the ComposableAgent plugin interface, automatically discovering tool schemas and mapping them to the T5 format for LLM tool calling. Supports multiple concurrent MCP server connections with isolated resource management and error handling per server.","intents":["Connect agents to external MCP servers (databases, APIs, file systems) without hardcoding tool definitions","Dynamically discover and register MCP tool schemas at runtime, enabling agents to adapt to new server capabilities","Build agent workflows that orchestrate multiple MCP servers (e.g., database query + file write + API call) in a single reasoning loop"],"best_for":["Developers integrating agents with MCP-compatible services (Anthropic Claude, Codebase tools, etc.)","Teams building extensible agent platforms where tools are added via MCP servers rather than code changes","Organizations standardizing on MCP for tool integration across multiple AI platforms"],"limitations":["MCP transport overhead: stdio-based servers add ~100-200ms per tool call due to process spawning and serialization","Schema discovery is synchronous and blocks agent startup; large numbers of MCP servers (10+) can add 5-10 seconds to initialization","Error handling is per-server; one failing MCP server can cascade failures if agent logic doesn't implement retry/fallback","No built-in caching of MCP schemas — each agent session rediscovers schemas, adding redundant overhead"],"requires":["MCP server implementations (stdio, SSE, or WebSocket transport)","Node.js 18+ for MCP client runtime","MCP protocol compliance (JSON-RPC 2.0 over specified transport)","Tool schema definitions in MCP format (JSON schema)"],"input_types":["MCP server connection details (stdio command, SSE URL, WebSocket endpoint)","tool invocation requests (tool name + arguments)","agent reasoning output (T5 format tool calls)"],"output_types":["discovered tool schemas (JSON schema format)","tool execution results (structured data or text)","error messages and server status","resource usage metrics per MCP server"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_3","uri":"capability://automation.workflow.browser.automation.with.headless.control.and.search.integration","name":"browser-automation-with-headless-control-and-search-integration","description":"Provides browser automation infrastructure for agents to control headless browsers (Chromium via Puppeteer/Playwright), capture DOM state, execute JavaScript, and interact with web pages. Integrates a search system layer that enables agents to perform web searches (via configurable search providers) and navigate results. The browser control layer abstracts page navigation, element interaction, and screenshot capture, feeding visual and DOM state back into the agent reasoning loop for next-step decisions.","intents":["Enable agents to browse the web, search for information, and interact with web applications programmatically","Provide agents with both visual (screenshot) and structural (DOM) understanding of web pages for more robust interaction","Automate web-based workflows (research, data collection, form submission) without manual browser control"],"best_for":["Agents that need to research information online or interact with web applications","Teams building web scraping or data collection workflows with AI reasoning","Organizations automating web-based business processes (booking, form filling, information gathering)"],"limitations":["Headless browser startup adds 2-5 seconds per session; reusing browser instances across multiple agent tasks is necessary for performance","JavaScript execution is asynchronous; agents must wait for page load and dynamic content rendering, adding latency","Search integration depends on external search provider APIs (Google, Bing, etc.); rate limiting and API costs apply","DOM extraction can be expensive for large, complex pages; no built-in optimization for selective DOM capture"],"requires":["Chromium/Chrome browser binary (Puppeteer downloads automatically)","Node.js 18+ for browser automation runtime","Search provider API keys (optional, for web search capability)","Network connectivity for web browsing and search"],"input_types":["URLs or search queries (text)","JavaScript code to execute in page context","interaction commands (click, type, scroll)","DOM selectors or visual coordinates"],"output_types":["screenshots (PNG/JPEG)","DOM state (HTML/JSON)","JavaScript execution results","search results (structured data with URLs and snippets)","page metadata (title, URL, status)"],"categories":["automation-workflow","search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_4","uri":"capability://code.generation.editing.code.execution.sandbox.with.isolated.runtime","name":"code-execution-sandbox-with-isolated-runtime","description":"Provides a Code Agent plugin that executes arbitrary code (Python, JavaScript, shell) in isolated sandbox environments, capturing stdout/stderr and execution results. Integrates with the Tarko framework to manage sandbox lifecycle, handle timeouts, and return execution results to the agent reasoning loop. Supports both local execution (for development) and remote sandbox services (for production isolation), with configurable resource limits and execution timeouts.","intents":["Enable agents to write and execute code to solve problems, analyze data, or perform computations","Provide agents with a safe, isolated environment for code execution without risking the host system","Allow agents to iterate on code solutions by capturing execution results and refining code based on errors"],"best_for":["Agents that need to perform data analysis, mathematical computations, or algorithm implementation","Teams building AI-assisted development tools where agents write and test code","Organizations requiring sandboxed code execution for security and isolation"],"limitations":["Sandbox startup latency: local sandboxes add 500ms-2s per execution; remote sandboxes add network round-trip overhead","Resource limits (memory, CPU, execution time) must be configured per sandbox; runaway code can exhaust resources","No persistent state between code executions; agents must pass data explicitly between execution steps","Limited to supported languages (Python, JavaScript, shell); other languages require custom sandbox implementations"],"requires":["Python 3.9+ (for Python code execution) or Node.js 18+ (for JavaScript)","Sandbox runtime (local subprocess or remote service like E2B, Replit, etc.)","Resource limits and timeout configuration","Network access for remote sandbox services (optional)"],"input_types":["code snippets (Python, JavaScript, shell)","execution context (environment variables, input data)","resource limits (timeout, memory, CPU)"],"output_types":["execution results (stdout, stderr, return value)","execution status (success, timeout, error)","resource usage metrics (execution time, memory used)","error traces and stack traces"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_5","uri":"capability://data.processing.analysis.t5.format.streaming.parser.for.structured.llm.output","name":"t5-format-streaming-parser-for-structured-llm-output","description":"Implements a T5 format streaming parser that converts LLM output (from vision-language models) into structured tool calls and reasoning traces. The parser handles partial/incomplete streaming responses, validates tool schemas against registered tools, and emits parsing events (tool_call, reasoning, error) that feed into the agent execution loop. Supports recovery from malformed output and provides detailed error messages for debugging LLM output issues.","intents":["Parse streaming LLM responses into structured tool calls without waiting for complete response","Validate tool invocations against registered tool schemas before execution, catching invalid calls early","Provide agents with structured reasoning traces and tool call history for debugging and transparency"],"best_for":["Developers building streaming agent systems that need real-time tool invocation","Teams integrating custom vision-language models with strict output format requirements","Organizations requiring detailed agent execution traces and reasoning transparency"],"limitations":["T5 format is proprietary to UI-TARS; LLMs must be fine-tuned or prompted to output this format, limiting model choice","Streaming parser state is stateful; connection interruptions or out-of-order chunks can corrupt parsing state","Schema validation adds overhead (~10-20ms per tool call); large numbers of tools (100+) can impact latency","Error recovery is limited; malformed output may require full response restart rather than graceful degradation"],"requires":["Vision-language model that outputs T5 format (Doubao-1.5-UI-TARS, or custom fine-tuned models)","Streaming API support (OpenAI-compatible streaming endpoints)","Tool schema definitions for validation","Node.js 18+ for parser runtime"],"input_types":["streaming LLM output (text chunks)","tool schema registry (JSON schema)","parsing configuration (error handling strategy)"],"output_types":["parsed tool calls (structured JSON)","reasoning traces (text)","parsing events (tool_call, reasoning, error)","validation errors with details"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_6","uri":"capability://automation.workflow.agent.session.lifecycle.management.with.event.streaming","name":"agent-session-lifecycle-management-with-event-streaming","description":"Manages agent session lifecycle (creation, execution, termination) through the Tarko Agent Server framework, which provides REST endpoints for session creation, query submission, and event streaming. Sessions maintain state (agent configuration, tool registry, execution history) and emit events (tool_call, reasoning, result, error) that are streamed to clients via Server-Sent Events (SSE) or WebSocket. Event storage persists execution history for audit, debugging, and session resumption.","intents":["Create and manage long-running agent sessions that maintain state across multiple user interactions","Stream agent execution events to frontend UIs or external systems in real-time for transparency and debugging","Persist agent execution history for audit trails, error analysis, and session resumption"],"best_for":["Teams building web-based agent UIs that need real-time event streaming and session management","Organizations requiring audit trails and execution history for compliance or debugging","Developers building agent platforms with multi-user session support"],"limitations":["Event streaming adds overhead: SSE connections consume server resources; high-concurrency deployments (1000+ sessions) require load balancing","Event storage can grow rapidly; long-running sessions with frequent events require database optimization or archival","Session state is in-memory by default; server restarts lose session state unless explicitly persisted","No built-in session clustering; multi-server deployments require external session store (Redis, database)"],"requires":["Node.js 18+ for Tarko Agent Server runtime","REST API client (curl, fetch, axios) for session management","SSE or WebSocket client for event streaming","Optional: database for event persistence (PostgreSQL, MongoDB, etc.)"],"input_types":["session creation request (agent config, model, tools)","query/instruction (text)","runtime settings (tool configuration, model parameters)"],"output_types":["session ID and metadata","event stream (JSON-formatted agent events)","execution history (structured event log)","session status and statistics"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_7","uri":"capability://automation.workflow.web.ui.configuration.and.dynamic.agent.composition","name":"web-ui-configuration-and-dynamic-agent-composition","description":"Provides a web-based UI (Tarko Agent Web UI) for configuring and composing agents without code, allowing users to select agent type (OmniTARS, GUI Agent, Code Agent), choose LLM provider and model, register tools (MCP servers, browser, code sandbox), and set runtime parameters. Configuration is serialized as JSON and passed to the agent server, enabling dynamic agent composition at runtime. The UI includes workspace navigation, session history, and content rendering for agent outputs.","intents":["Enable non-technical users to configure and launch AI agents without writing code","Allow teams to experiment with different agent configurations (models, tools, parameters) without redeployment","Provide a unified interface for managing multiple agent sessions and viewing execution history"],"best_for":["Non-technical users and product managers experimenting with agent capabilities","Teams building internal tools that need flexible agent configuration","Organizations deploying agents to end-users who need UI-based configuration"],"limitations":["Web UI is browser-based; complex configurations may be difficult to express through UI controls","No built-in version control for agent configurations; tracking configuration changes requires external tools","UI responsiveness depends on agent server latency; slow agents can make UI feel unresponsive","Limited to configurations supported by UI controls; advanced customizations require code changes"],"requires":["Web browser (Chrome, Firefox, Safari, Edge)","Tarko Agent Server running and accessible","Network connectivity to agent server","JavaScript enabled for interactive UI"],"input_types":["UI form inputs (dropdowns, text fields, toggles)","configuration JSON (for advanced users)","agent instructions (text)"],"output_types":["agent configuration (JSON)","session creation request","rendered agent outputs (text, code, images)","execution history and logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_8","uri":"capability://automation.workflow.electron.desktop.application.with.local.and.remote.control","name":"electron-desktop-application-with-local-and-remote-control","description":"Packages UI-TARS as a native Electron desktop application that provides local GUI automation (via GUIAgent SDK) and remote desktop control (via VNC/RDP). The Electron main process handles system permissions (screenshot, input simulation), manages local browser/sandbox processes, and communicates with remote desktop servers. The renderer process provides a React-based UI for configuration, session management, and real-time visualization of agent actions on the desktop.","intents":["Enable users to automate local desktop applications and workflows without command-line tools","Provide remote desktop automation capabilities with visual feedback and agent reasoning transparency","Offer a native desktop experience with system integration (permissions, notifications, file access)"],"best_for":["End-users automating local desktop workflows (data entry, repetitive tasks)","Teams managing remote desktops or virtual machines with AI-driven automation","Organizations deploying desktop automation tools to non-technical users"],"limitations":["Electron app size is large (~200-300MB); distribution and updates require significant bandwidth","System permissions (screenshot, input simulation) require user approval on macOS/Windows; permission denial breaks functionality","Remote desktop control (VNC/RDP) adds latency; real-time interaction is slower than local control","Desktop app is OS-specific; separate builds required for macOS, Windows, Linux"],"requires":["macOS 10.13+, Windows 10+, or Linux (Ubuntu 18.04+)","System permissions for screenshot and input simulation","VLM API key (for GUI automation)","VNC/RDP server (for remote desktop control, optional)"],"input_types":["desktop screenshots (framebuffer)","natural language task descriptions","remote desktop connection details (VNC/RDP)"],"output_types":["GUI automation actions (click, type, scroll)","screenshots with action visualization","execution logs and error messages","remote desktop stream (for VNC/RDP)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-bytedance-ui-tars-desktop__cap_9","uri":"capability://tool.use.integration.vlm.provider.abstraction.with.multi.model.support","name":"vlm-provider-abstraction-with-multi-model-support","description":"Abstracts vision-language model (VLM) providers through a configurable interface that supports OpenAI-compatible APIs, Anthropic Claude, and proprietary UI-TARS models (Doubao-1.5-UI-TARS). The VLM provider layer handles API authentication, request formatting, streaming response parsing, and error handling. 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