{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-gru-sandbox","slug":"gru-sandbox","name":"Gru Sandbox","type":"repo","url":"https://github.com/babelcloud/gru-sandbox","page_url":"https://unfragile.ai/gru-sandbox","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-gru-sandbox__cap_0","uri":"capability://tool.use.integration.mcp.server.sandbox.execution.with.process.isolation","name":"mcp server sandbox execution with process isolation","description":"Executes Model Context Protocol (MCP) servers in isolated sandbox environments with resource constraints and lifecycle management. Implements process-level isolation to prevent malicious or buggy MCP implementations from affecting the host system, with configurable memory limits, CPU quotas, and timeout enforcement. Manages server startup, health monitoring, and graceful shutdown through a containerized or process-based runtime.","intents":["I need to safely run untrusted MCP server implementations without risking my main application","I want to test multiple MCP server versions simultaneously without conflicts","I need to enforce resource limits on MCP servers to prevent runaway processes","I want to monitor and log MCP server behavior for debugging and auditing"],"best_for":["AI agent developers integrating third-party MCP servers","Teams building multi-tenant AI platforms with user-provided tools","Security-conscious organizations running untrusted code"],"limitations":["Sandbox overhead adds 50-200ms latency per MCP call depending on isolation mechanism","Cross-sandbox communication requires serialization, limiting performance for high-frequency calls","Resource limits must be tuned per workload; no automatic optimization"],"requires":["Node.js 16+ or Python 3.8+","Docker or Linux kernel namespaces for process isolation","MCP server implementation compatible with the sandbox runtime"],"input_types":["MCP server configuration (JSON/YAML)","Tool definitions and schemas","Request payloads from AI agents"],"output_types":["MCP server responses","Execution logs and metrics","Error traces and crash reports"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_1","uri":"capability://tool.use.integration.ai.agent.tool.registry.and.schema.validation","name":"ai agent tool registry and schema validation","description":"Maintains a centralized registry of available tools/MCP servers with JSON Schema validation for tool definitions, input parameters, and output contracts. Validates tool schemas at registration time and runtime to ensure type safety and prevent malformed requests from reaching sandboxed servers. Supports dynamic tool discovery and registration with conflict detection for duplicate tool names across multiple MCP servers.","intents":["I want to define and validate tool schemas before exposing them to AI agents","I need to prevent agents from calling tools with invalid parameters","I want to discover what tools are available and their capabilities at runtime","I need to handle conflicts when multiple MCP servers provide the same tool name"],"best_for":["AI agent developers managing complex tool ecosystems","Platform builders offering tool marketplaces","Teams implementing strict type safety for agent interactions"],"limitations":["Schema validation adds 10-50ms overhead per tool call","No built-in versioning for tool schemas; breaking changes require manual migration","Registry is in-memory by default; requires external persistence for multi-process deployments"],"requires":["JSON Schema draft 7 or later","Tool definitions must include explicit input/output schemas","Persistent storage backend (optional, for distributed deployments)"],"input_types":["JSON Schema definitions","Tool metadata (name, description, category)","Tool invocation requests with parameters"],"output_types":["Validated tool registry (JSON)","Schema validation errors","Tool discovery results"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_2","uri":"capability://tool.use.integration.multi.provider.mcp.server.orchestration.and.routing","name":"multi-provider mcp server orchestration and routing","description":"Routes tool requests from AI agents to appropriate MCP servers based on tool name, capability matching, or load-balancing policies. Implements request multiplexing across multiple MCP server instances, with automatic failover and retry logic. Abstracts away the complexity of managing multiple MCP server connections, allowing agents to call tools without knowing which server provides them.","intents":["I want to distribute tool calls across multiple MCP servers for load balancing","I need automatic failover if one MCP server becomes unavailable","I want agents to call tools without knowing which server provides them","I need to route requests based on tool capabilities or server specialization"],"best_for":["High-availability AI agent deployments","Multi-tenant platforms with per-tenant MCP servers","Teams running specialized MCP servers for different domains"],"limitations":["Routing logic adds 20-100ms latency depending on complexity","Failover retry logic may cause duplicate tool executions if not idempotent","No built-in load balancing metrics; requires external monitoring for optimal distribution"],"requires":["Multiple MCP server instances or configurations","Health check endpoints for failover detection","Idempotent tool implementations for safe retries"],"input_types":["Tool invocation requests","Routing policies (JSON configuration)","Server health status"],"output_types":["Tool execution results","Routing decisions and metrics","Failover and retry logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_3","uri":"capability://code.generation.editing.sandboxed.code.execution.for.agent.tools","name":"sandboxed code execution for agent tools","description":"Executes arbitrary code (Python, JavaScript, shell scripts) within isolated sandbox environments triggered by agent tool calls. Implements filesystem isolation, network restrictions, and resource limits to prevent code from accessing sensitive data or consuming excessive resources. Captures stdout/stderr and execution results, with timeout enforcement and crash recovery.","intents":["I want agents to execute custom code without risking the host system","I need to run user-provided scripts safely as part of agent workflows","I want to prevent code from accessing files outside a specific directory","I need to enforce execution timeouts to prevent infinite loops"],"best_for":["AI platforms allowing user-defined tools or plugins","Research environments running untrusted agent code","Multi-tenant SaaS platforms executing per-user code"],"limitations":["Sandbox overhead adds 100-500ms per execution depending on isolation mechanism","Network access restrictions may break legitimate use cases requiring external APIs","Filesystem isolation requires careful configuration to avoid breaking legitimate file operations"],"requires":["Docker, gVisor, or OS-level sandboxing (seccomp, AppArmor)","Python 3.8+ and/or Node.js 16+ for code execution","Sufficient disk space for sandbox images or temporary files"],"input_types":["Code snippets (Python, JavaScript, shell)","Execution context and environment variables","Input data for the code to process"],"output_types":["Execution results (stdout, stderr)","Return values or exit codes","Execution metrics (duration, memory used)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_4","uri":"capability://automation.workflow.agent.request.response.logging.and.audit.trail","name":"agent request/response logging and audit trail","description":"Captures and persists all agent requests, tool invocations, and responses with full context including timestamps, parameters, results, and execution metadata. Implements structured logging with queryable audit trails for compliance, debugging, and performance analysis. Supports filtering, searching, and exporting logs for external analysis or compliance reporting.","intents":["I need to audit what tools agents called and what results they received","I want to debug agent behavior by reviewing the full execution trace","I need to comply with regulations requiring audit trails of system actions","I want to analyze agent performance and identify bottlenecks"],"best_for":["Regulated industries requiring compliance audit trails","Teams debugging complex agent behaviors","Performance-sensitive deployments needing execution metrics"],"limitations":["Logging adds 5-20ms overhead per request depending on storage backend","High-volume deployments may require log rotation and archival strategies","Sensitive data in logs requires encryption and access controls"],"requires":["Persistent storage backend (file system, database, or cloud logging service)","Log retention policy and cleanup strategy","Encryption for sensitive data in logs (optional but recommended)"],"input_types":["Agent requests and tool invocations","Tool responses and execution results","System events and errors"],"output_types":["Structured log entries (JSON)","Audit reports and summaries","Queryable log archives"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_5","uri":"capability://automation.workflow.self.hosted.deployment.with.docker.kubernetes.support","name":"self-hosted deployment with docker/kubernetes support","description":"Provides containerized deployment configurations (Docker, Docker Compose, Kubernetes manifests) for running Gru Sandbox in self-hosted environments. Includes pre-built container images, environment variable configuration, and orchestration templates for scaling across multiple nodes. Supports both single-machine and distributed deployments with persistent storage backends.","intents":["I want to deploy Gru Sandbox in my own infrastructure without relying on cloud services","I need to scale the sandbox across multiple machines for high availability","I want to integrate Gru Sandbox into my existing Kubernetes cluster","I need to configure resource limits and networking for my deployment"],"best_for":["Organizations with strict data residency requirements","Teams managing their own infrastructure","Deployments requiring custom networking or security policies"],"limitations":["Requires operational expertise in Docker/Kubernetes for production deployments","Self-hosting requires managing updates, backups, and disaster recovery","Scaling requires external load balancing and service discovery configuration"],"requires":["Docker 20.10+ or Kubernetes 1.20+","Persistent storage backend (local volumes, NFS, or cloud storage)","Network configuration for inter-pod communication"],"input_types":["Docker Compose files or Kubernetes manifests","Environment variable configurations","Volume mount specifications"],"output_types":["Running container instances","Service endpoints and health status","Deployment logs and metrics"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_6","uri":"capability://automation.workflow.configuration.management.for.sandbox.policies.and.constraints","name":"configuration management for sandbox policies and constraints","description":"Manages sandbox execution policies through declarative configuration (YAML/JSON) including resource limits (CPU, memory, disk), network access rules, filesystem permissions, and timeout settings. Applies policies at sandbox creation time and enforces them throughout execution. Supports policy inheritance and overrides for different tool categories or user groups.","intents":["I want to define different resource limits for different types of tools","I need to restrict network access for certain sandboxes while allowing it for others","I want to enforce execution timeouts to prevent runaway processes","I need to control which files sandboxes can access"],"best_for":["Multi-tenant platforms with per-tenant policies","Organizations with strict security policies","Teams managing diverse tool ecosystems with different requirements"],"limitations":["Policy changes require sandbox restart to take effect","Complex policy rules may be difficult to reason about and debug","No built-in policy conflict detection; overlapping rules require manual resolution"],"requires":["YAML or JSON configuration files","Understanding of resource limits and security policies","Restart capability for applying policy changes"],"input_types":["Policy configuration files (YAML/JSON)","Resource limit specifications","Network and filesystem access rules"],"output_types":["Enforced sandbox constraints","Policy validation errors","Policy audit logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_7","uri":"capability://automation.workflow.health.monitoring.and.liveness.probes.for.mcp.servers","name":"health monitoring and liveness probes for mcp servers","description":"Continuously monitors MCP server health through configurable health check endpoints and liveness probes. Detects server crashes, hangs, or degraded performance and triggers automatic recovery actions (restart, failover, alerting). Exposes health metrics and status for external monitoring systems and dashboards.","intents":["I want to detect when an MCP server becomes unresponsive","I need automatic recovery when a server crashes","I want to monitor server health metrics over time","I need to alert operations when servers are unhealthy"],"best_for":["Production deployments requiring high availability","Teams with limited on-call capacity","Platforms with SLA requirements"],"limitations":["Health checks add network overhead; frequent checks may impact performance","False positives can trigger unnecessary restarts, causing service disruption","Recovery actions (restart, failover) may lose in-flight requests"],"requires":["Health check endpoint on MCP servers","Configurable probe frequency and timeout settings","External monitoring system for alerting (optional)"],"input_types":["Health check configuration (endpoint, frequency, timeout)","Server status and metrics"],"output_types":["Health status (healthy, degraded, unhealthy)","Metrics and performance data","Recovery action logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_8","uri":"capability://tool.use.integration.request.response.transformation.and.middleware.pipeline","name":"request/response transformation and middleware pipeline","description":"Implements a middleware pipeline for transforming agent requests before sending to MCP servers and transforming responses before returning to agents. Supports custom middleware for authentication, request validation, response formatting, caching, and rate limiting. Middleware executes in configurable order with early termination on errors.","intents":["I want to add authentication headers to MCP server requests","I need to validate requests before they reach sandboxed servers","I want to cache tool responses to reduce execution overhead","I need to rate-limit tool calls per user or agent"],"best_for":["Multi-tenant platforms requiring per-tenant authentication","Teams implementing caching or rate-limiting strategies","Deployments with complex request/response transformation needs"],"limitations":["Middleware pipeline adds 10-50ms latency depending on number and complexity of middleware","Middleware ordering is critical; incorrect order can cause subtle bugs","Caching middleware requires careful invalidation logic to prevent stale responses"],"requires":["Middleware implementation framework or plugin system","Understanding of request/response lifecycle","Configuration for middleware ordering and parameters"],"input_types":["Agent requests","Middleware configuration","Cached responses (for caching middleware)"],"output_types":["Transformed requests","Transformed responses","Middleware execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gru-sandbox__cap_9","uri":"capability://automation.workflow.error.handling.and.graceful.degradation.for.tool.failures","name":"error handling and graceful degradation for tool failures","description":"Implements comprehensive error handling for tool execution failures including timeouts, crashes, invalid responses, and network errors. Provides fallback strategies (retry with backoff, alternative tool selection, graceful degradation) and error context for agent decision-making. Distinguishes between transient and permanent failures to inform recovery strategies.","intents":["I want agents to retry failed tool calls with exponential backoff","I need to provide meaningful error messages when tools fail","I want agents to fall back to alternative tools when primary tool fails","I need to distinguish between transient and permanent failures"],"best_for":["Resilient agent deployments requiring fault tolerance","Multi-tool systems where fallbacks are available","Teams implementing sophisticated error recovery strategies"],"limitations":["Retry logic increases latency for failed requests","Distinguishing transient vs permanent failures requires domain knowledge","Fallback strategies may produce different results than primary tool"],"requires":["Error classification and handling rules","Retry policy configuration (max attempts, backoff strategy)","Fallback tool definitions (optional)"],"input_types":["Tool execution errors","Error context and metadata","Retry and fallback policies"],"output_types":["Retry decisions and results","Fallback tool selections","Error reports and analytics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ or Python 3.8+","Docker or Linux kernel namespaces for process isolation","MCP server implementation compatible with the sandbox runtime","JSON Schema draft 7 or later","Tool definitions must include explicit input/output schemas","Persistent storage backend (optional, for distributed deployments)","Multiple MCP server instances or configurations","Health check endpoints for failover detection","Idempotent tool implementations for safe retries","Docker, gVisor, or OS-level sandboxing (seccomp, AppArmor)"],"failure_modes":["Sandbox overhead adds 50-200ms latency per MCP call depending on isolation mechanism","Cross-sandbox communication requires serialization, limiting performance for high-frequency calls","Resource limits must be tuned per workload; no automatic optimization","Schema validation adds 10-50ms overhead per tool call","No built-in versioning for tool schemas; breaking changes require manual migration","Registry is in-memory by default; requires external persistence for multi-process deployments","Routing logic adds 20-100ms latency depending on complexity","Failover retry logic may cause duplicate tool executions if not idempotent","No built-in load balancing metrics; requires external monitoring for optimal distribution","Sandbox overhead adds 100-500ms per execution depending on isolation mechanism","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=gru-sandbox","compare_url":"https://unfragile.ai/compare?artifact=gru-sandbox"}},"signature":"VzjJaSbl40Ptzn3tg9i3PI+v6ZQtv/Luyjee0Y2la9V8JZdYwMnNvj+GmrCs12woleF5iMwEzBxhq6BIn1raBg==","signedAt":"2026-06-21T12:20:39.489Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gru-sandbox","artifact":"https://unfragile.ai/gru-sandbox","verify":"https://unfragile.ai/api/v1/verify?slug=gru-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"}}