{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-agent-infra-sandbox","slug":"mcp-agent-infra-sandbox","name":"sandbox","type":"mcp","url":"https://github.com/agent-infra/sandbox","page_url":"https://unfragile.ai/mcp-agent-infra-sandbox","categories":["mcp-servers"],"tags":["agent","all-in-one","browser","filesystem","mcp","sandbox","shell"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-agent-infra-sandbox__cap_0","uri":"capability://automation.workflow.unified.file.system.across.runtimes","name":"unified-file-system-across-runtimes","description":"Provides a single shared file system at /home/gem that is accessible across all integrated runtimes (browser, shell, Jupyter, Node.js, VSCode) without requiring external storage coordination or data transfer between sandboxes. Files downloaded via browser automation are instantly available to shell commands and code execution endpoints, eliminating the fragmentation problem of separate execution environments.","intents":["I want files downloaded by a browser agent to be immediately usable in shell commands without manual transfer","I need to share data between Jupyter notebooks and shell scripts running in the same sandbox","I want to avoid building ETL pipelines between isolated execution environments"],"best_for":["AI agent developers building multi-step workflows that span browser, code, and shell execution","data scientists combining web scraping with local data processing","teams migrating from fragmented sandbox architectures to unified environments"],"limitations":["File system is ephemeral per container instance — no persistence across container restarts without external volume mounts","Concurrent file access from multiple runtimes requires application-level locking (not enforced by sandbox)","Performance degrades with large files (>1GB) due to Docker volume I/O overhead"],"requires":["Docker runtime with volume support","File system permissions configured for /home/gem directory","All services running in same container instance"],"input_types":["files","directories","symlinks"],"output_types":["files","directories","file metadata"],"categories":["automation-workflow","file-system-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_1","uri":"capability://tool.use.integration.browser.automation.with.chromium.integration","name":"browser-automation-with-chromium-integration","description":"Provides headless Chromium browser automation through a REST API and MCP protocol interface, supporting navigation, interaction, screenshot capture, and DOM inspection. The browser shares the unified file system, allowing downloaded files and captured data to be immediately available to other sandbox components without external storage. Integrates with browser-use framework for agent-driven web automation workflows.","intents":["I want an AI agent to navigate websites, fill forms, and extract data without managing browser lifecycle","I need to capture screenshots and DOM state during web automation for debugging or analysis","I want downloaded files from browser automation to be available to shell scripts immediately"],"best_for":["AI agents performing web scraping and form automation","developers building browser-use framework integrations","teams needing headless browser execution without local Chromium installation"],"limitations":["Chromium runs in headless mode only — no GPU acceleration, limiting performance for complex rendering","JavaScript execution is sandboxed — cannot access host system APIs or break out of browser context","Screenshot and DOM capture operations add 200-500ms latency per request due to serialization overhead"],"requires":["Docker container with Chromium binary pre-installed","Minimum 512MB RAM allocated to browser process","REST API endpoint or MCP client connection to sandbox"],"input_types":["URL","CSS selectors","XPath expressions","JavaScript code"],"output_types":["screenshot (PNG/JPEG)","DOM tree (JSON/HTML)","extracted text","downloaded files"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_10","uri":"capability://automation.workflow.vnc.remote.desktop.interface","name":"vnc-remote-desktop-interface","description":"Provides VNC (Virtual Network Computing) access to a remote desktop environment within the container, enabling human operators to visually interact with the sandbox. The VNC server displays the Chromium browser, terminal, and other GUI applications running in the container. Useful for debugging agent workflows, monitoring browser automation, and manual intervention.","intents":["I want to visually monitor browser automation in real-time to debug agent behavior","I need to manually interact with the sandbox GUI when an agent gets stuck","I want to see what the agent is seeing during web automation"],"best_for":["developers debugging browser automation workflows","teams needing visual monitoring of agent execution","operators performing manual intervention during agent failures"],"limitations":["VNC adds 100-300ms latency per frame due to network transmission and compression","VNC server consumes 50-100MB memory and 10-20% CPU for display rendering","Remote desktop interaction is slower than local GUI due to network latency","VNC connection requires network access to container — not suitable for air-gapped environments"],"requires":["Docker container with VNC server (e.g., TigerVNC)","VNC client software (e.g., RealVNC, TightVNC)","Network access to sandbox container on VNC port (default 5900)"],"input_types":["mouse movements","keyboard input","mouse clicks"],"output_types":["desktop screenshot (bitmap)","display updates (RFB protocol)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_11","uri":"capability://automation.workflow.docker.container.deployment.with.compose","name":"docker-container-deployment-with-compose","description":"Provides Docker container image and Docker Compose configuration for easy local and cloud deployment. The container bundles all sandbox components (browser, shell, Jupyter, VSCode, MCP server, REST API) into a single image with pre-configured networking and volume mounts. Supports deployment to Docker, Kubernetes, and cloud platforms (Volcengine VEFAAS, etc.).","intents":["I want to run the sandbox locally with a single docker-compose up command","I need to deploy the sandbox to Kubernetes for production use","I want to customize the sandbox image with additional tools or dependencies"],"best_for":["developers deploying sandbox locally for development","teams deploying sandbox to Kubernetes clusters","organizations using cloud container platforms (Volcengine, AWS ECS, etc.)"],"limitations":["Docker Compose is suitable for single-node deployments only — use Kubernetes for multi-node scaling","Container image is large (~2GB) due to bundled runtimes (Chromium, Jupyter, VSCode)","Persistent storage requires external volume configuration — container state is ephemeral by default","Custom image creation requires Docker build knowledge and may increase image size further"],"requires":["Docker 20.10+ or Docker Desktop","Docker Compose 2.0+ (for compose.yaml syntax)","Minimum 4GB RAM and 10GB disk space for container image","Network access for pulling base images from registries"],"input_types":["docker-compose.yaml configuration","Dockerfile for custom image"],"output_types":["running container instance","container logs (stdout/stderr)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_12","uri":"capability://tool.use.integration.langchain.integration.with.tool.bindings","name":"langchain-integration-with-tool-bindings","description":"Provides LangChain integration patterns and examples for using sandbox capabilities as LangChain tools. The integration includes tool wrappers that expose browser, shell, file, and code execution as LangChain-compatible tools with proper error handling and output formatting. Enables LangChain agents to orchestrate sandbox capabilities seamlessly.","intents":["I want to use sandbox capabilities in a LangChain agent without writing custom tool wrappers","I need proper error handling and output formatting for sandbox tools in LangChain workflows","I want to combine sandbox tools with other LangChain tools (web search, calculator, etc.)"],"best_for":["LangChain developers building AI agents with sandbox capabilities","teams standardizing on LangChain for agent development","developers needing pre-built tool integrations to reduce boilerplate"],"limitations":["LangChain integration requires LangChain 0.1+ — older versions may not be compatible","Tool wrappers add 50-100ms overhead per tool call due to serialization and error handling","Error messages from sandbox are passed through as-is — may not be user-friendly for agents","Tool output is limited to 4096 characters to fit within LLM context windows"],"requires":["Python 3.8+","LangChain 0.1+","Sandbox Python SDK","API authentication token for sandbox"],"input_types":["LangChain tool invocation (ToolCall objects)"],"output_types":["LangChain tool result (ToolResult objects)","error messages (text)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_13","uri":"capability://tool.use.integration.browser.use.framework.integration","name":"browser-use-framework-integration","description":"Provides integration with the browser-use framework, enabling agents to use browser automation through browser-use's high-level API. The integration includes examples and documentation for combining browser-use with sandbox's shell, file, and code execution capabilities. Enables agents to perform complex web automation workflows with browser-use's agent-friendly abstractions.","intents":["I want to use browser-use framework with sandbox's browser automation","I need to combine browser-use web automation with shell commands and code execution","I want to leverage browser-use's agent-friendly abstractions for complex web workflows"],"best_for":["developers using browser-use framework for web automation","teams building complex web automation agents","developers needing high-level browser abstractions instead of low-level APIs"],"limitations":["browser-use framework adds abstraction overhead — slightly slower than direct browser API calls","browser-use requires specific browser capabilities — not all sandbox browser features are exposed","Integration examples are provided but may require customization for specific use cases","browser-use agent loop adds 500ms-2s overhead per action due to LLM inference"],"requires":["Python 3.8+","browser-use framework","Sandbox Python SDK","LLM API key (OpenAI, Anthropic, etc.) for browser-use agent"],"input_types":["browser-use task description (natural language)"],"output_types":["browser-use action results (structured)","screenshots (PNG/JPEG)","extracted data (JSON)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_14","uri":"capability://planning.reasoning.skills.system.for.agent.capabilities","name":"skills-system-for-agent-capabilities","description":"Implements a skills system that packages sandbox capabilities into reusable, composable skills that agents can discover and invoke. Skills are defined with schemas, documentation, and execution logic. The system enables agents to understand available capabilities and combine them into complex workflows without hardcoding tool calls.","intents":["I want agents to discover available sandbox capabilities through a skills registry","I need to package common workflows (e.g., 'download and analyze file') as reusable skills","I want agents to compose skills into complex multi-step workflows"],"best_for":["teams building agent frameworks with composable capabilities","developers creating skill libraries for specific domains","organizations standardizing on skill-based agent architectures"],"limitations":["Skills system adds abstraction overhead — skill composition is slower than direct API calls","Skill schemas must be manually defined — no automatic schema generation from code","Skill discovery requires agents to understand skill registry format — not standardized across frameworks","Skill composition logic is agent-specific — no universal skill composition language"],"requires":["Sandbox REST API or MCP server","Agent framework with skills support (custom implementation required)","Skill schema definitions (JSON or similar format)"],"input_types":["skill invocation requests (JSON)"],"output_types":["skill execution results (JSON)","skill schemas (JSON Schema format)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_15","uri":"capability://automation.workflow.dashboard.ui.for.monitoring.and.control","name":"dashboard-ui-for-monitoring-and-control","description":"Provides a web-based dashboard UI for monitoring sandbox status, viewing execution logs, and controlling sandbox operations. The dashboard displays active processes, file system state, execution history, and resource usage. Enables operators to monitor agent execution, inspect results, and trigger manual operations without CLI access.","intents":["I want to monitor agent execution status and resource usage in real-time","I need to inspect execution logs and debug agent failures through a web interface","I want to trigger manual operations (restart services, clear files) without SSH access"],"best_for":["operators monitoring agent execution in production","teams needing web-based monitoring without CLI expertise","developers debugging agent workflows through a visual interface"],"limitations":["Dashboard adds 100-200MB memory overhead to container","Real-time updates require WebSocket connection — may not work behind restrictive proxies","Dashboard is read-only for most operations — manual control is limited to restart/clear operations","Log retention is limited to prevent disk exhaustion — old logs are automatically purged"],"requires":["Docker container with dashboard service","Web browser with JavaScript support","Network access to sandbox container on dashboard port"],"input_types":["user interactions (clicks, form submissions)"],"output_types":["dashboard UI (HTML/CSS/JavaScript)","real-time updates (WebSocket)","logs and metrics (JSON)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_16","uri":"capability://planning.reasoning.evaluation.framework.for.agent.testing","name":"evaluation-framework-for-agent-testing","description":"Provides an evaluation framework for testing and benchmarking AI agents running in the sandbox. The framework includes evaluation datasets, agent loop implementations, and metrics collection for assessing agent performance. Supports custom evaluation scenarios and automated testing of agent workflows.","intents":["I want to test my AI agent against a standard evaluation dataset","I need to measure agent performance metrics (success rate, latency, cost)","I want to compare different agent implementations using the same evaluation framework"],"best_for":["researchers evaluating AI agent performance","teams benchmarking agent implementations","developers testing agent workflows before production deployment"],"limitations":["Evaluation framework requires custom agent loop implementation — not all agent frameworks are supported","Evaluation datasets are limited to provided scenarios — custom scenarios require manual creation","Metrics collection adds overhead to agent execution — may affect performance measurements","Evaluation results are specific to sandbox environment — may not generalize to other environments"],"requires":["Python 3.8+","Sandbox Python SDK","Agent implementation compatible with evaluation framework","Evaluation dataset (provided or custom)"],"input_types":["evaluation scenarios (JSON)","agent implementation (Python code)"],"output_types":["evaluation results (JSON)","performance metrics (CSV/JSON)","execution logs (text)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_2","uri":"capability://automation.workflow.shell.command.execution.with.environment.isolation","name":"shell-command-execution-with-environment-isolation","description":"Executes arbitrary shell commands (bash/sh) in an isolated process within the container, with access to the shared /home/gem file system and environment variables. Commands run with configurable working directory, timeout limits, and output capture (stdout/stderr). Supports both synchronous execution and streaming output for long-running processes.","intents":["I want an AI agent to run system commands (git, curl, ffmpeg, etc.) without managing subprocess lifecycle","I need to capture command output and exit codes for error handling in agent workflows","I want to run long-running processes with streaming output instead of waiting for completion"],"best_for":["AI agents performing DevOps tasks (deployment, configuration management)","developers building CLI tool orchestration workflows","teams needing isolated command execution without host system access"],"limitations":["No inter-process communication (IPC) with host system — commands cannot access host sockets or devices","Command execution timeout defaults to 30 seconds; long-running processes require explicit timeout configuration","Environment variables are inherited from container startup — dynamic variable injection requires API call per command","Output buffering limits stdout/stderr to 10MB per command to prevent memory exhaustion"],"requires":["Docker container with bash/sh shell","Appropriate file system permissions for /home/gem","REST API endpoint or MCP client connection"],"input_types":["shell command string","environment variables (key-value pairs)","working directory path"],"output_types":["stdout (text)","stderr (text)","exit code (integer)","execution duration (milliseconds)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_3","uri":"capability://code.generation.editing.stateful.jupyter.kernel.execution","name":"stateful-jupyter-kernel-execution","description":"Provides persistent Jupyter kernel execution with state preservation across multiple requests, enabling interactive data science workflows. Kernels maintain variable scope, imported libraries, and execution history within a session. Supports Python code execution with full access to installed packages and the shared /home/gem file system. Exposes both REST API and JupyterLab web interface for interactive development.","intents":["I want to run Python code with persistent state across multiple agent requests (e.g., loading a dataset once, then querying it multiple times)","I need interactive debugging and variable inspection during AI agent workflows","I want to use Jupyter notebooks for exploratory data analysis within the sandbox"],"best_for":["data scientists building AI agents that perform iterative analysis","teams needing interactive Python execution with state preservation","developers debugging complex agent workflows with notebook-style introspection"],"limitations":["Kernel state is lost on container restart — requires external persistence layer for long-term state","Memory usage grows unbounded with large variable assignments — no automatic garbage collection across requests","Concurrent kernel requests from multiple agents can cause race conditions if accessing shared state","JupyterLab web interface adds 50-100ms latency per cell execution due to serialization overhead"],"requires":["Docker container with Jupyter and Python runtime","Python 3.8+","Minimum 1GB RAM for kernel process","REST API endpoint or JupyterLab web interface"],"input_types":["Python code (string)","cell execution requests"],"output_types":["execution result (JSON serializable)","stdout/stderr (text)","variable state (JSON)","error traceback (text)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_4","uri":"capability://code.generation.editing.stateless.code.execution.nodejs.python","name":"stateless-code-execution-nodejs-python","description":"Executes isolated, stateless Node.js and Python scripts in separate processes with no state preservation between requests. Each execution is sandboxed with its own environment, preventing cross-contamination between agent requests. Supports quick script runs with configurable timeout and output capture. Useful for one-off computations, transformations, and utility functions.","intents":["I want to run a quick Python or Node.js script without managing process lifecycle or state","I need isolated execution to prevent one agent request from affecting another","I want to execute utility functions (JSON parsing, data transformation) with minimal overhead"],"best_for":["AI agents performing stateless transformations and utility operations","developers building function-as-a-service style workflows","teams needing isolated script execution without cross-request contamination"],"limitations":["No state preservation between requests — each execution starts with clean environment","Script startup overhead (process creation, module loading) adds 100-300ms per execution","Maximum script size limited to 1MB to prevent memory exhaustion","No inter-script communication — each execution is completely isolated"],"requires":["Docker container with Node.js and/or Python runtime","Node.js 16+ or Python 3.8+","REST API endpoint or MCP client connection"],"input_types":["Node.js code (string)","Python code (string)","function arguments (JSON)"],"output_types":["execution result (JSON serializable)","stdout/stderr (text)","error traceback (text)","execution duration (milliseconds)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_5","uri":"capability://tool.use.integration.rest.api.with.auto.generated.sdks","name":"rest-api-with-auto-generated-sdks","description":"Exposes all sandbox capabilities through a REST API (/v1/* endpoints) with auto-generated Python and TypeScript/JavaScript SDKs using Fern framework. The API provides programmatic control over browser, shell, file, and code execution with standardized request/response formats. SDKs abstract HTTP details and provide type-safe interfaces for agent integration.","intents":["I want to control the sandbox from Python or TypeScript without writing HTTP requests manually","I need type-safe SDK methods for IDE autocomplete and compile-time error checking","I want to integrate sandbox capabilities into LangChain agents or other AI frameworks"],"best_for":["Python and TypeScript developers building AI agents","teams using LangChain or similar frameworks requiring SDK integration","developers preferring type-safe interfaces over raw HTTP APIs"],"limitations":["SDK generation requires Fern schema maintenance — API changes require schema updates and SDK regeneration","HTTP overhead adds 50-200ms latency per request compared to direct process calls","SDKs are auto-generated, limiting customization for agent-specific use cases","Authentication is token-based — requires API key management and rotation"],"requires":["Python 3.8+ (for Python SDK) or Node.js 16+ (for TypeScript SDK)","REST API endpoint accessible over HTTP/HTTPS","API authentication token"],"input_types":["SDK method calls with typed arguments"],"output_types":["typed response objects","error exceptions with structured details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_6","uri":"capability://tool.use.integration.model.context.protocol.mcp.server","name":"model-context-protocol-mcp-server","description":"Implements the Model Context Protocol (MCP) standard, exposing sandbox tools as MCP resources and tools that can be discovered and invoked by MCP-compatible AI agents. The MCP server provides standardized tool schemas, enabling agents to understand capabilities without custom integration code. Supports tool discovery, schema validation, and streaming responses for long-running operations.","intents":["I want my AI agent to discover and use sandbox capabilities through the MCP standard without custom integration","I need standardized tool schemas so my agent can understand what each sandbox capability does","I want to use the same agent code with multiple MCP-compatible sandboxes"],"best_for":["AI agents using MCP-compatible frameworks (Claude, other LLM agents)","teams standardizing on MCP for tool integration across multiple services","developers building agent frameworks that support MCP"],"limitations":["MCP protocol overhead adds 100-300ms per tool invocation compared to direct API calls","Tool schema validation is strict — malformed requests are rejected before execution","Streaming responses require MCP client support — not all agents handle streaming tools","MCP server requires separate connection management (stdio, HTTP, SSE) depending on transport"],"requires":["MCP-compatible AI agent or framework","MCP client library (e.g., Claude SDK with MCP support)","Sandbox REST API endpoint for underlying tool execution"],"input_types":["MCP tool invocation requests (JSON-RPC format)"],"output_types":["MCP tool response (JSON-RPC format)","tool schemas (JSON Schema format)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_7","uri":"capability://code.generation.editing.vscode.server.code.editor.integration","name":"vscode-server-code-editor-integration","description":"Runs VS Code Server (/code-server endpoint) within the container, providing a web-based code editor with full IDE features (syntax highlighting, debugging, extensions, terminal). The editor has direct access to the shared /home/gem file system and can execute code through the sandbox's Python/Node.js/shell execution endpoints. Enables human developers to interactively edit and debug agent workflows.","intents":["I want to edit and debug agent code in a web-based IDE without installing VS Code locally","I need to inspect files created by browser automation or shell commands in a familiar editor","I want to run and test code snippets interactively during agent development"],"best_for":["developers debugging AI agent workflows interactively","teams needing remote development environments without local setup","developers combining code editing with browser automation and shell execution"],"limitations":["VS Code Server adds 200-500MB memory overhead to container","Web-based editor has higher latency than native VS Code due to HTTP/WebSocket overhead","Extension ecosystem is limited compared to native VS Code (some extensions require native binaries)","Debugging requires additional configuration and may not support all language runtimes"],"requires":["Docker container with VS Code Server binary","Web browser with WebSocket support","Minimum 512MB RAM for VS Code Server process","Network access to sandbox container"],"input_types":["file edits (text)","terminal commands (shell)"],"output_types":["edited files (text)","terminal output (text)","debug information (structured)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_8","uri":"capability://code.generation.editing.jupyterlab.interactive.notebook.interface","name":"jupyterlab-interactive-notebook-interface","description":"Runs JupyterLab (/jupyter endpoint) within the container, providing an interactive notebook interface for exploratory data analysis and code development. Notebooks have access to the shared /home/gem file system and can execute Python code through the stateful Jupyter kernel. Supports markdown documentation, rich output visualization, and cell-by-cell execution.","intents":["I want to develop and test agent code interactively using Jupyter notebooks","I need to visualize data and results from browser automation or shell commands","I want to document agent workflows with markdown and code together"],"best_for":["data scientists and researchers developing AI agents","teams using notebooks for exploratory agent development","developers needing rich output visualization (plots, tables, HTML)"],"limitations":["JupyterLab adds 300-500MB memory overhead to container","Notebook execution is single-threaded — long-running cells block other operations","Output size is limited to prevent memory exhaustion — large plots or tables may be truncated","Notebook state is lost on container restart without external persistence"],"requires":["Docker container with JupyterLab and Python runtime","Python 3.8+","Web browser with JavaScript support","Minimum 512MB RAM for JupyterLab process"],"input_types":["Python code (cells)","markdown (documentation)"],"output_types":["execution results (text, HTML, plots)","stdout/stderr (text)","variable state (JSON)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-agent-infra-sandbox__cap_9","uri":"capability://data.processing.analysis.file.operations.api.with.unified.access","name":"file-operations-api-with-unified-access","description":"Provides REST API endpoints for file operations (read, write, delete, list, upload, download) on the shared /home/gem file system. Supports batch operations, directory traversal, and metadata queries. All file operations are atomic and respect file system permissions. Integrates with browser downloads and code execution output, enabling seamless file sharing across sandbox components.","intents":["I want to upload files to the sandbox for processing by agents","I need to download results (screenshots, extracted data, generated files) from agent execution","I want to list and inspect files created by browser automation or code execution"],"best_for":["AI agents performing file-based workflows (upload, process, download)","developers building data pipelines that combine multiple sandbox components","teams needing programmatic file access without SSH or direct file system access"],"limitations":["File upload/download size is limited to 500MB per request to prevent memory exhaustion","Batch operations have a maximum of 100 files per request","File permissions are inherited from container user — no fine-grained ACL support","Symbolic links are not followed to prevent directory traversal attacks"],"requires":["REST API endpoint accessible over HTTP/HTTPS","File system permissions configured for /home/gem directory","API authentication token"],"input_types":["file content (binary or text)","file paths (strings)","directory paths (strings)"],"output_types":["file content (binary or text)","file metadata (JSON)","directory listing (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Docker runtime with volume support","File system permissions configured for /home/gem directory","All services running in same container instance","Docker container with Chromium binary pre-installed","Minimum 512MB RAM allocated to browser process","REST API endpoint or MCP client connection to sandbox","Docker container with VNC server (e.g., TigerVNC)","VNC client software (e.g., RealVNC, TightVNC)","Network access to sandbox container on VNC port (default 5900)","Docker 20.10+ or Docker Desktop"],"failure_modes":["File system is ephemeral per container instance — no persistence across container restarts without external volume mounts","Concurrent file access from multiple runtimes requires application-level locking (not enforced by sandbox)","Performance degrades with large files (>1GB) due to Docker volume I/O overhead","Chromium runs in headless mode only — no GPU acceleration, limiting performance for complex rendering","JavaScript execution is sandboxed — cannot access host system APIs or break out of browser context","Screenshot and DOM capture operations add 200-500ms latency per request due to serialization overhead","VNC adds 100-300ms latency per frame due to network transmission and compression","VNC server consumes 50-100MB memory and 10-20% CPU for display rendering","Remote desktop interaction is slower than local GUI due to network latency","VNC connection requires network access to container — not suitable for air-gapped environments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5737114082234589,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-04-10T03:28:44Z"},"community":{"stars":4510,"forks":388,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-agent-infra-sandbox","compare_url":"https://unfragile.ai/compare?artifact=mcp-agent-infra-sandbox"}},"signature":"yX5fX9ts1+wPzYgsPajtB14R6WoBDKtrpxz1xScIDL2VFLpOs64N4imOQovd2z+HkeDptUlFPcmodz20WindCw==","signedAt":"2026-06-21T01:46:32.891Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-agent-infra-sandbox","artifact":"https://unfragile.ai/mcp-agent-infra-sandbox","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-agent-infra-sandbox","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"}}