{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-kubernetes","slug":"kubernetes","name":"Kubernetes","type":"mcp","url":"https://github.com/Flux159/mcp-server-kubernetes","page_url":"https://unfragile.ai/kubernetes","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-kubernetes__cap_0","uri":"capability://tool.use.integration.mcp.standardized.kubernetes.cluster.connection.and.authentication","name":"mcp-standardized kubernetes cluster connection and authentication","description":"Establishes secure connections to Kubernetes clusters through the Model Context Protocol (MCP) transport layer, supporting multiple authentication methods including kubeconfig files, service account tokens, and in-cluster authentication. The KubernetesManager component loads and manages kubeconfig credentials, handles context/namespace switching, and maintains API client lifecycle across multiple cluster configurations. Supports stdio, SSE, and HTTP transports for flexible client integration patterns.","intents":["Connect an AI assistant to a Kubernetes cluster without exposing raw kubectl access","Support multiple cluster contexts and switch between them programmatically","Authenticate through service accounts when running inside Kubernetes","Use in-cluster authentication for pod-to-cluster communication"],"best_for":["DevOps teams integrating Kubernetes management into AI-powered workflows","Platform engineers building multi-cluster management interfaces","Organizations requiring standardized API access to Kubernetes instead of direct CLI"],"limitations":["Requires kubeconfig file or in-cluster service account — cannot use ad-hoc credentials","Transport layer adds latency overhead compared to direct kubectl invocation","Context/namespace switching is per-request, not persistent across tool calls","No built-in connection pooling — creates new API client per request in some configurations"],"requires":["Kubernetes cluster 1.19+","Valid kubeconfig file or in-cluster service account token","Node.js 18+ for MCP server runtime","Network connectivity to Kubernetes API server"],"input_types":["kubeconfig file path (string)","cluster context name (string)","namespace name (string)","service account token (string)"],"output_types":["authenticated API client instance","connection status (success/failure)","available contexts and namespaces (structured data)"],"categories":["tool-use-integration","authentication"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_1","uri":"capability://tool.use.integration.kubectl.operation.wrapping.with.command.injection.prevention","name":"kubectl operation wrapping with command injection prevention","description":"Wraps kubectl CLI commands into structured MCP tools with built-in command injection prevention through argument sanitization and schema validation. Each kubectl operation (get, apply, delete, exec, logs) is exposed as a discrete MCP tool with typed parameters that are validated before shell execution. Uses parameterized command construction rather than string interpolation to prevent shell metacharacter injection attacks.","intents":["Execute kubectl commands through an AI assistant without exposing shell injection vulnerabilities","Provide structured, type-safe access to kubectl operations with validated parameters","Audit and log all kubectl operations executed through the MCP interface","Restrict which kubectl operations are available based on security policies"],"best_for":["Security-conscious teams deploying AI-assisted Kubernetes management","Organizations with compliance requirements for kubectl audit trails","Teams wanting to expose Kubernetes operations to LLMs without direct shell access"],"limitations":["Cannot execute arbitrary kubectl plugins — only built-in kubectl commands are wrapped","Complex kubectl flags with special characters may require escaping","No streaming output support for long-running operations like kubectl port-forward","Parameter validation adds ~50-100ms overhead per operation"],"requires":["kubectl CLI installed and in PATH","Kubernetes cluster with appropriate RBAC permissions for the authenticated user","Node.js 18+"],"input_types":["resource type (string: pod, deployment, service, etc.)","resource name (string)","namespace (string)","output format (string: json, yaml, wide)","command-specific parameters (typed objects)"],"output_types":["kubectl command output (JSON or YAML structured data)","command execution status (success/failure with error details)","resource state snapshots (structured Kubernetes objects)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_10","uri":"capability://safety.moderation.custom.tool.filtering.and.capability.restriction","name":"custom tool filtering and capability restriction","description":"Restricts which MCP tools are available to clients through server-side configuration, allowing operators to disable specific operations (e.g., disable pod exec, disable resource deletion). Filtering is configured at server startup and applied uniformly across all clients. Provides explicit tool availability metadata to clients.","intents":["Restrict AI assistants to specific Kubernetes operations","Disable dangerous operations like pod exec in production","Enforce organizational policies about which operations are allowed","Provide different tool sets for different deployment environments"],"best_for":["Organizations with strict operational policies about which tools can be used","Production environments where certain operations are forbidden","Multi-tenant deployments where different clients need different capabilities"],"limitations":["Tool filtering is global — cannot be configured per-client","Filtering is all-or-nothing — cannot restrict specific parameters of a tool","Changes require server restart — no dynamic tool reconfiguration","Clients cannot discover which tools are filtered — must handle errors gracefully"],"requires":["Server configuration at startup (tool whitelist/blacklist)","No client-side configuration needed"],"input_types":["tool names to filter (array of strings, configured at startup)"],"output_types":["available tools list (array of tool definitions with filtered tools excluded)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_11","uri":"capability://tool.use.integration.multi.transport.protocol.support.stdio.sse.http","name":"multi-transport protocol support (stdio, sse, http)","description":"Supports multiple MCP transport mechanisms for client integration: stdio for local CLI tools and VS Code extensions, Server-Sent Events (SSE) for browser-based clients, and HTTP for REST-style integrations. Transport selection is automatic based on client connection method. Each transport handles message framing, error handling, and connection lifecycle independently.","intents":["Integrate with VS Code extensions using stdio transport","Connect browser-based AI interfaces using SSE transport","Support REST-style clients using HTTP transport","Deploy single MCP server supporting multiple client types"],"best_for":["Organizations with diverse client ecosystems (CLI, web, IDE extensions)","Teams wanting single MCP server deployment supporting multiple integration patterns","Developers building custom MCP clients with specific transport requirements"],"limitations":["Stdio transport is synchronous — cannot handle concurrent requests","SSE transport is unidirectional — server cannot initiate requests to clients","HTTP transport requires additional authentication layer (not built-in)","Transport switching requires server restart — no dynamic transport reconfiguration"],"requires":["Node.js 18+ for all transports","For stdio: local process execution capability","For SSE: HTTP server and client with SSE support","For HTTP: HTTP server and client libraries"],"input_types":["transport type (determined by client connection method)"],"output_types":["MCP protocol messages (JSON-RPC format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_12","uri":"capability://automation.workflow.opentelemetry.observability.and.distributed.tracing","name":"opentelemetry observability and distributed tracing","description":"Integrates OpenTelemetry for distributed tracing, metrics collection, and logging across all MCP operations. Exports traces to observability backends (Jaeger, Datadog, New Relic) with automatic span creation for each tool invocation. Includes metrics for operation latency, error rates, and resource utilization. Traces include full context propagation for multi-step workflows.","intents":["Monitor MCP server performance and operation latency","Debug multi-step Kubernetes workflows with distributed tracing","Collect metrics on tool usage and error rates","Integrate with existing observability infrastructure"],"best_for":["Production deployments requiring observability and performance monitoring","Teams using distributed tracing for multi-service architectures","Organizations with centralized observability platforms (Datadog, New Relic, etc.)"],"limitations":["OpenTelemetry configuration is complex — requires understanding of exporters and collectors","Tracing adds ~5-10% performance overhead per operation","Traces are not persisted locally — require external observability backend","Sensitive data (secrets, API keys) may be captured in traces — requires careful filtering"],"requires":["OpenTelemetry SDK for Node.js","Observability backend (Jaeger, Datadog, New Relic, etc.)","Network connectivity to observability backend","Configuration of OpenTelemetry exporters"],"input_types":["OpenTelemetry configuration (environment variables or config file)"],"output_types":["distributed traces (exported to observability backend)","metrics (operation latency, error rates, resource utilization)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_13","uri":"capability://planning.reasoning.interactive.prompts.and.guided.workflows","name":"interactive prompts and guided workflows","description":"Provides MCP prompts that guide users through complex Kubernetes operations with step-by-step instructions and context-aware suggestions. Prompts are dynamically generated based on cluster state and can include resource recommendations, troubleshooting steps, and deployment checklists. Implements prompt templates that clients can invoke to start guided workflows.","intents":["Guide users through complex Kubernetes operations with step-by-step prompts","Provide context-aware suggestions based on cluster state","Create reusable workflow templates for common operations","Reduce cognitive load for users unfamiliar with Kubernetes"],"best_for":["Teams with mixed Kubernetes expertise levels","Organizations wanting to standardize operational procedures","AI assistants that benefit from structured guidance for complex tasks"],"limitations":["Prompts are static templates — cannot adapt to real-time cluster changes","Prompt generation adds latency (~100-500ms per prompt)","Complex workflows may require multiple prompt invocations","Clients must support MCP prompts — not all MCP clients implement this feature"],"requires":["MCP client with prompt support (Claude Desktop, some VS Code extensions)","Cluster connectivity for generating context-aware prompts"],"input_types":["prompt name (string: deployment-checklist, troubleshooting-pod, etc.)","context parameters (object: namespace, resource type, etc.)"],"output_types":["prompt text (string with markdown formatting)","suggested next steps (array of strings)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_14","uri":"capability://automation.workflow.deployment.and.configuration.management.across.environments","name":"deployment and configuration management across environments","description":"Supports multiple deployment patterns: NPM package installation for local development, Docker container deployment for cloud environments, and Helm chart deployment for Kubernetes-native installations. Includes environment-specific configuration through environment variables, config files, and Helm values. Manages multi-cluster configurations with context switching.","intents":["Deploy MCP server in development, staging, and production environments","Configure server for different Kubernetes clusters and authentication methods","Manage environment-specific settings (operation modes, tool filtering, observability)","Deploy server as Kubernetes workload managing other clusters"],"best_for":["DevOps teams deploying MCP servers across multiple environments","Organizations with multi-cluster Kubernetes deployments","Teams wanting to manage MCP server configuration as code"],"limitations":["Configuration is environment-specific — requires separate deployments per environment","Helm chart deployment requires Kubernetes cluster — cannot deploy to non-Kubernetes environments","Docker deployment requires container registry and orchestration platform","Configuration changes require server restart — no dynamic reconfiguration"],"requires":["For NPM: Node.js 18+","For Docker: Docker runtime and container registry","For Helm: Kubernetes cluster and Helm 3.0+","Kubeconfig file or service account token for cluster access"],"input_types":["deployment method (npm, docker, helm)","environment variables (configuration)","kubeconfig file (cluster authentication)"],"output_types":["deployed server instance","server status and health checks"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_2","uri":"capability://data.processing.analysis.resource.query.and.filtering.with.structured.output","name":"resource query and filtering with structured output","description":"Executes kubectl get operations with structured output parsing, returning Kubernetes resources as typed JSON objects with optional filtering, sorting, and field selection. Supports querying pods, deployments, services, configmaps, secrets, and other resource types with output format negotiation (JSON, YAML, wide table). Implements server-side filtering through kubectl selectors and client-side filtering through response post-processing.","intents":["Query Kubernetes resources and receive structured data suitable for AI analysis","Filter resources by labels, field selectors, or custom predicates","Extract specific fields from resources without parsing raw kubectl output","Monitor resource state changes by comparing sequential queries"],"best_for":["AI assistants analyzing cluster state and making decisions based on resource status","Observability integrations that need structured resource snapshots","Automated troubleshooting workflows that query resource metrics and conditions"],"limitations":["Large result sets (>1000 resources) may cause memory pressure and slow JSON parsing","Real-time resource changes between query and response may cause stale data","Field selection is limited to kubectl-supported JSONPath expressions","No built-in pagination — returns all matching resources in single response"],"requires":["kubectl CLI with get command support","Read permissions on target resource types via RBAC","Kubernetes cluster 1.19+"],"input_types":["resource type (string: pods, deployments, services, etc.)","namespace (string, optional)","label selector (string, e.g., 'app=myapp')","field selector (string, e.g., 'status.phase=Running')","output format (string: json, yaml, wide)"],"output_types":["array of Kubernetes resource objects (JSON)","resource count (integer)","filtered/selected fields (structured data)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_3","uri":"capability://automation.workflow.resource.creation.and.modification.with.yaml.json.templating","name":"resource creation and modification with yaml/json templating","description":"Creates and modifies Kubernetes resources by accepting YAML or JSON manifests, optionally applying Helm templating before submission to the cluster. Supports kubectl apply (declarative) and kubectl create (imperative) operations with conflict resolution strategies. Implements template mode for Helm charts, allowing parameterized resource generation before kubectl apply.","intents":["Create Kubernetes resources from manifests generated or edited by an AI assistant","Apply declarative configuration changes to existing resources","Use Helm templating to generate parameterized manifests before deployment","Validate manifests before applying them to the cluster"],"best_for":["GitOps workflows where AI assists in manifest generation and application","Teams using Helm charts and needing programmatic template rendering","Automated deployment pipelines triggered by AI decision-making"],"limitations":["No dry-run validation by default — requires explicit dry-run flag to preview changes","Large manifests (>1MB) may exceed kubectl processing limits","Helm templating requires Helm CLI installed and chart repositories configured","No built-in conflict resolution for concurrent modifications — relies on kubectl merge strategies"],"requires":["kubectl CLI with apply/create command support","Write permissions on target resource types via RBAC","Helm CLI 3.0+ if using template mode","Valid YAML or JSON manifest syntax"],"input_types":["manifest content (string: YAML or JSON)","operation mode (string: apply or create)","namespace (string)","helm values (object, optional for template mode)","dry-run flag (boolean)"],"output_types":["operation result (success/failure with details)","created/modified resource metadata (name, kind, namespace)","dry-run preview (resource diff, optional)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_4","uri":"capability://automation.workflow.resource.deletion.with.safety.guards.and.cascading.options","name":"resource deletion with safety guards and cascading options","description":"Deletes Kubernetes resources with configurable cascading deletion policies (orphan, background, foreground) and optional dry-run preview. Implements non-destructive mode that prevents deletion operations entirely, and read-only mode that blocks all write operations. Supports bulk deletion through label selectors and field selectors.","intents":["Delete Kubernetes resources with confirmation and safety checks","Perform cascading deletions of parent resources with controlled child cleanup","Preview deletion impact before executing with dry-run mode","Enforce read-only policies to prevent accidental resource deletion"],"best_for":["Production environments where deletion operations require explicit approval","Teams with strict change control policies requiring deletion previews","Automated cleanup workflows that need to respect cascading deletion semantics"],"limitations":["Non-destructive mode must be configured at server startup — cannot be toggled per-request","Dry-run mode shows deletion intent but not actual impact on dependent resources","Cascading deletion policies are Kubernetes-level semantics — server cannot override them","Bulk deletion by selector may delete unintended resources if selector is too broad"],"requires":["kubectl CLI with delete command support","Delete permissions on target resource types via RBAC","Kubernetes cluster 1.19+"],"input_types":["resource type (string: pod, deployment, service, etc.)","resource name (string, optional for bulk deletion)","namespace (string)","label selector (string, optional for bulk deletion)","cascading policy (string: orphan, background, foreground)","dry-run flag (boolean)"],"output_types":["deletion result (success/failure with details)","deleted resource metadata (name, kind, namespace)","dry-run preview (deletion impact, optional)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_5","uri":"capability://automation.workflow.pod.execution.and.log.streaming.with.container.selection","name":"pod execution and log streaming with container selection","description":"Executes commands inside running pods and retrieves pod logs through kubectl exec and kubectl logs commands. Supports multi-container pod selection, interactive command execution with stdin/stdout capture, and log filtering by container, timestamps, and line count. Implements timeout controls to prevent long-running exec sessions from blocking the MCP server.","intents":["Execute diagnostic commands inside pods for troubleshooting","Retrieve application logs for analysis and debugging","Capture pod output for AI-assisted log analysis and anomaly detection","Debug multi-container pods by selecting specific containers"],"best_for":["Troubleshooting workflows where AI assists in diagnosing pod issues","Log analysis pipelines that need programmatic access to pod output","Automated remediation systems that execute diagnostic commands in pods"],"limitations":["Interactive exec sessions are not truly interactive — stdin is not supported, only command execution","Log streaming is not real-time — returns historical logs only, no tail/follow capability","Timeout controls may truncate long-running command output","Multi-container selection requires explicit container name — no automatic selection"],"requires":["kubectl CLI with exec and logs command support","Exec permissions on pods via RBAC","Pod must be in Running state for exec operations","Kubernetes cluster 1.19+"],"input_types":["pod name (string)","namespace (string)","container name (string, optional)","command (string or array of strings for exec)","log options (object: tail lines, timestamps, since duration)"],"output_types":["command output (string: stdout/stderr combined)","pod logs (string: formatted log lines)","execution status (success/failure with exit code)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_6","uri":"capability://automation.workflow.helm.chart.deployment.and.release.management","name":"helm chart deployment and release management","description":"Manages Helm releases through helm install, helm upgrade, and helm uninstall commands with values file support and template rendering. Supports Helm chart repositories, local chart paths, and inline values objects. Implements release history tracking and rollback capabilities through helm rollback operations.","intents":["Deploy Helm charts to Kubernetes clusters with parameterized values","Upgrade existing Helm releases with new chart versions or values","Rollback failed Helm releases to previous versions","Query Helm release status and history"],"best_for":["Teams using Helm for package management and deployment automation","GitOps workflows where Helm charts are the source of truth","Multi-environment deployments with environment-specific Helm values"],"limitations":["Requires Helm CLI 3.0+ installed and configured","Chart repositories must be pre-configured — server cannot add repositories dynamically","Large values objects may exceed command-line argument limits","Rollback only works for releases managed by Helm — cannot rollback kubectl apply manifests"],"requires":["Helm CLI 3.0+ installed and in PATH","Helm chart repositories configured (helm repo add)","Write permissions on target namespace via RBAC","Kubernetes cluster 1.19+"],"input_types":["release name (string)","chart reference (string: repo/chart or local path)","namespace (string)","values object (object: key-value pairs)","chart version (string, optional)"],"output_types":["release metadata (name, namespace, status, version)","deployment result (success/failure with details)","release history (array of revisions with timestamps)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_7","uri":"capability://search.retrieval.cluster.wide.resource.discovery.and.introspection","name":"cluster-wide resource discovery and introspection","description":"Discovers available Kubernetes resource types, API groups, and cluster capabilities through kubectl api-resources and kubectl api-versions commands. Provides cluster metadata including version, node count, and available storage classes. Implements resource schema introspection for understanding resource structure and required fields.","intents":["Discover what resource types are available in a cluster","Understand cluster capabilities and API versions","Retrieve resource schemas for validation and generation","Determine cluster version compatibility for manifests"],"best_for":["AI assistants that need to understand cluster capabilities before generating manifests","Automated manifest generation systems that validate against cluster schemas","Multi-cluster management tools that need to detect cluster differences"],"limitations":["Schema introspection is limited to kubectl api-resources output — no full OpenAPI schema access","Cluster metadata is static snapshot — does not reflect real-time cluster state","Custom resource definitions (CRDs) are discovered but their schemas require separate queries","No caching — each discovery query hits the API server"],"requires":["kubectl CLI with api-resources and api-versions support","Read permissions on API discovery endpoints via RBAC","Kubernetes cluster 1.19+"],"input_types":["none — discovery is cluster-wide"],"output_types":["available resource types (array of strings)","API groups and versions (structured data)","cluster metadata (version, node count, etc.)","resource schemas (structured object definitions)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_8","uri":"capability://safety.moderation.secrets.masking.and.sensitive.data.redaction","name":"secrets masking and sensitive data redaction","description":"Automatically redacts sensitive data (secrets, passwords, API keys) from kubectl output and logs before returning to MCP clients. Implements pattern-based masking for common secret types (docker-registry, generic, tls) and custom regex patterns for application-specific secrets. Redaction happens at the response layer before data reaches the client.","intents":["Prevent accidental exposure of secrets when querying Kubernetes resources","Redact sensitive data from logs and command output","Maintain audit trails without exposing credentials","Comply with security policies that restrict secret visibility"],"best_for":["Security-conscious organizations sharing Kubernetes access with AI assistants","Compliance-heavy environments (healthcare, finance) with strict data handling requirements","Teams using AI for troubleshooting but wanting to restrict secret access"],"limitations":["Masking is pattern-based — custom secret formats may not be detected","Redaction happens after kubectl execution — secrets are briefly in memory","Cannot selectively unmask secrets for authorized clients — all-or-nothing redaction","Performance overhead of regex matching on large outputs (~5-10% for typical responses)"],"requires":["Server-side configuration of masking patterns","No additional client configuration required"],"input_types":["masking patterns (regex strings, configured at server startup)","secret types to mask (strings: docker-registry, generic, tls, custom)"],"output_types":["redacted kubectl output (string with secrets replaced by [REDACTED])","redacted logs (string with sensitive data masked)"],"categories":["safety-moderation","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kubernetes__cap_9","uri":"capability://safety.moderation.non.destructive.and.read.only.operation.modes","name":"non-destructive and read-only operation modes","description":"Enforces server-wide operation modes that restrict tool availability: non-destructive mode disables deletion and modification operations, read-only mode disables all write operations. Modes are configured at server startup and applied uniformly across all clients. Provides explicit error messages when clients attempt restricted operations.","intents":["Restrict AI assistants to read-only access for safety in production environments","Allow querying and analysis without risk of accidental modifications","Enforce organizational policies that prevent certain operations","Provide audit-safe access where all operations are non-destructive"],"best_for":["Production environments where AI should only observe, not modify","Compliance-heavy organizations requiring strict operation controls","Teams onboarding AI assistants with limited initial permissions"],"limitations":["Modes are global — cannot be toggled per-client or per-request","Read-only mode prevents all writes including resource creation","Non-destructive mode still allows modifications — only prevents deletions","Mode changes require server restart — no dynamic reconfiguration"],"requires":["Server configuration at startup (environment variables or config file)","No client-side configuration needed"],"input_types":["operation mode (string: normal, non-destructive, read-only, configured at startup)"],"output_types":["operation result (success for allowed operations, error for restricted operations)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Kubernetes cluster 1.19+","Valid kubeconfig file or in-cluster service account token","Node.js 18+ for MCP server runtime","Network connectivity to Kubernetes API server","kubectl CLI installed and in PATH","Kubernetes cluster with appropriate RBAC permissions for the authenticated user","Node.js 18+","Server configuration at startup (tool whitelist/blacklist)","No client-side configuration needed","Node.js 18+ for all transports"],"failure_modes":["Requires kubeconfig file or in-cluster service account — cannot use ad-hoc credentials","Transport layer adds latency overhead compared to direct kubectl invocation","Context/namespace switching is per-request, not persistent across tool calls","No built-in connection pooling — creates new API client per request in some configurations","Cannot execute arbitrary kubectl plugins — only built-in kubectl commands are wrapped","Complex kubectl flags with special characters may require escaping","No streaming output support for long-running operations like kubectl port-forward","Parameter validation adds ~50-100ms overhead per operation","Tool filtering is global — cannot be configured per-client","Filtering is all-or-nothing — cannot restrict specific parameters of a tool","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kubernetes","compare_url":"https://unfragile.ai/compare?artifact=kubernetes"}},"signature":"RFLFnRJyOtCr8MZ4OFdH6bpxEclwrbuIiPzJ3wyWSnW3utbDwYABLWpODa+/ZlVJa+CvDFxQZgM5c9m0Z4U3Aw==","signedAt":"2026-06-22T15:19:51.943Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kubernetes","artifact":"https://unfragile.ai/kubernetes","verify":"https://unfragile.ai/api/v1/verify?slug=kubernetes","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"}}