{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mkp","slug":"mkp","name":"MKP","type":"mcp","url":"https://github.com/StacklokLabs/mkp","page_url":"https://unfragile.ai/mkp","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mkp__cap_0","uri":"capability://tool.use.integration.kubernetes.resource.retrieval.via.mcp.protocol","name":"kubernetes resource retrieval via mcp protocol","description":"Retrieves specific Kubernetes resources or their subresources (status, scale, logs) by translating MCP tool calls into direct Kubernetes API requests using the unstructured client library. Supports both clustered and namespaced resources with standardized k8s:// URI parsing, enabling LLMs to fetch resource state without CLI knowledge. Implements server-side filtering and subresource path resolution for accessing derived resource views.","intents":["I need an LLM to fetch the current state of a specific Kubernetes deployment or pod","I want to retrieve pod logs or deployment status through an LLM agent without exposing kubectl","I need to access Kubernetes subresources like /status or /scale endpoints programmatically"],"best_for":["LLM-powered Kubernetes operators and automation agents","Teams building AI-driven cluster management tools","Organizations needing LLM-safe read-only cluster introspection"],"limitations":["Subresource access limited to those exposed by Kubernetes API server (logs, status, scale)","No built-in caching — each request hits the Kubernetes API directly, adding ~50-200ms latency per call","Unstructured client returns generic map[string]interface{} — requires LLM to parse YAML/JSON structure"],"requires":["Kubernetes cluster 1.19+ with accessible API server","Valid kubeconfig or in-cluster service account with read permissions","MCP-compatible LLM client (Claude, OpenAI with MCP support, or custom MCP consumer)"],"input_types":["MCP tool call with resource URI (k8s://clustered/{group}/{version}/{resource}/{name})","MCP tool call with namespaced URI (k8s://namespaced/{namespace}/{group}/{version}/{resource}/{name})"],"output_types":["JSON-serialized Kubernetes resource object","Structured resource metadata with spec, status, and annotations"],"categories":["tool-use-integration","kubernetes-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_1","uri":"capability://tool.use.integration.kubernetes.resource.listing.with.type.discovery","name":"kubernetes resource listing with type discovery","description":"Lists all resources of a specified type (Deployments, Pods, Services, or any CRD) across the cluster or within a namespace by querying the Kubernetes API discovery layer and then issuing list requests. Implements server-side filtering by namespace and resource type, returning paginated results as JSON arrays. Supports both clustered (cluster-scoped) and namespaced resources with automatic API group/version resolution.","intents":["I want an LLM to discover all pods in a namespace and summarize their status","I need to list all custom resources of a specific type to find misconfigured instances","I want to enumerate available Kubernetes resource types the cluster supports"],"best_for":["Cluster auditing and compliance agents powered by LLMs","Multi-resource discovery and troubleshooting workflows","Teams building LLM-driven cluster health dashboards"],"limitations":["Pagination not exposed to MCP layer — returns all results in single response, may be slow for large clusters (1000+ resources)","No label selector or field selector support — filters only by namespace and resource type","API discovery calls add ~100-300ms overhead on first list operation per resource type"],"requires":["Kubernetes cluster 1.19+ with accessible API server","Service account with list permissions for target resource types","MCP-compatible LLM client"],"input_types":["MCP tool call specifying resource type and optional namespace","Resource URI pattern for clustered or namespaced resources"],"output_types":["JSON array of Kubernetes resource objects","Metadata including resource names, namespaces, creation timestamps"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_10","uri":"capability://tool.use.integration.minimalist.resource.operation.api.surface","name":"minimalist resource operation api surface","description":"Exposes only core Kubernetes operations (list, get, apply) as MCP tools, avoiding feature bloat and maintaining a clean, maintainable codebase. Implements focused tool schemas that map directly to Kubernetes API operations without abstraction layers. Prioritizes reliability and performance over feature completeness.","intents":["I want a simple, predictable Kubernetes interface for LLM agents","I need a lightweight tool that doesn't require extensive configuration","I want to avoid complex abstractions that hide Kubernetes semantics"],"best_for":["Teams prioritizing simplicity and maintainability","Organizations with straightforward Kubernetes automation needs","Developers building custom LLM-Kubernetes workflows"],"limitations":["No delete operation — resources must be deleted via kubectl or other tools","No watch/streaming for resource changes — only point-in-time queries","No advanced filtering (label selectors, field selectors) — only namespace and resource type","No batch operations — each resource requires separate API call"],"requires":["Acceptance of limited operation set (list, get, apply only)"],"input_types":["MCP tool calls for list, get, apply operations"],"output_types":["Kubernetes resource objects"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_2","uri":"capability://tool.use.integration.kubernetes.resource.creation.and.update.via.apply.semantics","name":"kubernetes resource creation and update via apply semantics","description":"Creates or updates Kubernetes resources by accepting YAML/JSON manifests and applying them using Kubernetes server-side apply or client-side merge semantics. Translates MCP tool calls into unstructured client apply operations, handling both clustered and namespaced resources. Implements conflict resolution and field ownership tracking to enable safe concurrent updates from multiple LLM agents.","intents":["I want an LLM to create a new Deployment or ConfigMap based on user requirements","I need to update an existing resource (e.g., scale a deployment) through an LLM agent","I want safe concurrent updates from multiple LLM agents without overwriting each other's changes"],"best_for":["LLM-driven infrastructure provisioning and GitOps automation","Multi-agent Kubernetes management systems","Teams building AI-powered cluster configuration tools"],"limitations":["Requires write permissions on target resources — no fine-grained field-level RBAC enforcement at MKP layer","No validation of manifest syntax before apply — relies on Kubernetes API server validation, adding round-trip latency","Server-side apply conflicts may require LLM to retry with conflict resolution logic — not automatic","No rollback capability — failed applies leave partial state in cluster"],"requires":["Kubernetes cluster 1.19+ (1.22+ recommended for server-side apply)","Service account with create/update/patch permissions for target resource types","Valid YAML/JSON manifest as input"],"input_types":["YAML or JSON Kubernetes manifest","MCP tool call with resource definition and namespace"],"output_types":["Applied resource object with updated status","Conflict error details if server-side apply fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_3","uri":"capability://tool.use.integration.mcp.protocol.server.with.llm.tool.binding","name":"mcp protocol server with llm tool binding","description":"Implements the Model Context Protocol (MCP) server specification, exposing Kubernetes operations as standardized MCP tools (get_resource, list_resources, apply_resource) that LLM clients can discover and invoke. Handles MCP request/response serialization, tool schema definition, and error propagation back to LLM applications. Supports both stdio and SSE transport mechanisms for different LLM client architectures.","intents":["I want to integrate Kubernetes operations into Claude or other MCP-compatible LLMs","I need to expose Kubernetes as a tool that an LLM agent can discover and call","I want standardized tool schemas so LLMs understand Kubernetes resource parameters"],"best_for":["Teams building LLM agents that need Kubernetes access","Organizations standardizing on MCP for LLM tool integration","Developers integrating Kubernetes with Claude or other MCP-aware LLMs"],"limitations":["MCP protocol overhead adds ~20-50ms per tool call for serialization/deserialization","Tool schemas are static — cannot dynamically adapt to cluster-specific CRD schemas","No built-in authentication between MCP client and server — relies on transport-level security (TLS for SSE, process isolation for stdio)","SSE transport requires HTTP/2 support and persistent connections, may not work behind certain proxies"],"requires":["MCP-compatible LLM client (Claude 3.5+, OpenAI with MCP plugin, or custom MCP consumer)","Go 1.21+ for building from source","Network connectivity between LLM client and MKP server"],"input_types":["MCP tool call requests with JSON parameters","MCP resource requests for URI-based access"],"output_types":["MCP tool result with JSON-serialized Kubernetes objects","MCP error responses with structured error details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_4","uri":"capability://tool.use.integration.server.sent.events.sse.streaming.for.real.time.updates","name":"server-sent events (sse) streaming for real-time updates","description":"Provides Server-Sent Events transport for MCP protocol communication, enabling persistent HTTP connections between LLM clients and MKP server for streaming resource updates and watch events. Implements SSE-compliant event serialization and connection lifecycle management. Allows LLM applications to subscribe to cluster changes without polling.","intents":["I want an LLM to receive real-time notifications when Kubernetes resources change","I need to stream pod logs or deployment status updates to an LLM agent","I want to establish a persistent connection for long-running LLM-driven cluster monitoring"],"best_for":["Real-time cluster monitoring and alerting agents","LLM-driven incident response systems","Teams building streaming Kubernetes dashboards with LLM analysis"],"limitations":["SSE requires HTTP/2 and persistent connections — may not work behind HTTP/1.1-only proxies or load balancers","No built-in reconnection logic — client must handle connection drops and re-subscribe","Watch events not implemented — SSE server currently provides request/response transport only, not event streaming","Memory overhead for persistent connections — scales linearly with number of connected LLM clients"],"requires":["HTTP/2-capable network and load balancer","LLM client with SSE support (custom implementation required)","TLS/HTTPS for production deployments"],"input_types":["HTTP POST requests with MCP tool calls","SSE connection establishment"],"output_types":["Server-Sent Events stream with MCP responses","Real-time resource change notifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_5","uri":"capability://tool.use.integration.kubernetes.api.discovery.and.schema.resolution","name":"kubernetes api discovery and schema resolution","description":"Dynamically discovers available Kubernetes resource types and their API groups/versions by querying the cluster's API discovery endpoints (/api/v1, /apis). Resolves resource URIs to correct API group, version, and resource name without requiring pre-configured schemas. Supports both built-in resources and Custom Resource Definitions (CRDs) with automatic schema detection.","intents":["I want to list all resource types available in a cluster without hardcoding them","I need to resolve a resource URI to the correct Kubernetes API endpoint","I want to support CRDs without modifying MKP code"],"best_for":["Multi-cluster environments with varying CRD configurations","Teams building cluster-agnostic LLM tools","Organizations with custom Kubernetes extensions (operators, CRDs)"],"limitations":["Discovery calls add 100-300ms latency on first access per resource type — no caching across server restarts","CRD schema validation not performed — relies on Kubernetes API server validation","API discovery results cached in-memory only — cluster changes (new CRDs) require server restart to discover","No support for deprecated API versions — uses latest available version automatically"],"requires":["Kubernetes cluster 1.19+ with accessible API discovery endpoints","Service account with read permissions on APIResourceList"],"input_types":["Resource URI with group, version, and resource name","Resource type name (e.g., 'Deployment', 'Pod')"],"output_types":["Resolved API group and version","Resource metadata (namespaced vs clustered, subresources)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_6","uri":"capability://safety.moderation.kubeconfig.and.in.cluster.authentication","name":"kubeconfig and in-cluster authentication","description":"Authenticates to Kubernetes clusters using kubeconfig files (for local development) or in-cluster service account tokens (for pod deployments). Implements automatic credential detection and client certificate/token management. Supports multiple cluster contexts and context switching for multi-cluster scenarios.","intents":["I want to run MKP locally against a dev cluster using kubeconfig","I need to deploy MKP as a pod with automatic service account authentication","I want to manage multiple cluster contexts from a single MKP instance"],"best_for":["Development teams testing LLM-Kubernetes integration locally","Production deployments of MKP in Kubernetes clusters","Multi-cluster management scenarios"],"limitations":["In-cluster authentication uses default service account — requires explicit RBAC role binding for each resource type","Kubeconfig file must be readable by MKP process — no secrets management integration (Vault, etc.)","No credential rotation — service account tokens valid until pod restart","Context switching requires server restart — no dynamic context switching at runtime"],"requires":["Valid kubeconfig file (for local deployment) or Kubernetes service account (for in-cluster)","RBAC permissions matching intended operations (list, get, create, patch, etc.)"],"input_types":["Kubeconfig file path or in-cluster token","Cluster context name"],"output_types":["Authenticated Kubernetes client connection"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_7","uri":"capability://safety.moderation.namespaced.and.cluster.scoped.resource.differentiation","name":"namespaced and cluster-scoped resource differentiation","description":"Automatically distinguishes between namespaced resources (Pods, Deployments, ConfigMaps) and cluster-scoped resources (Nodes, ClusterRoles, PersistentVolumes) using Kubernetes API metadata. Routes requests to correct API endpoints and enforces namespace constraints for namespaced resources. Implements separate URI schemes (k8s://namespaced/ vs k8s://clustered/) for explicit scope specification.","intents":["I want an LLM to safely list resources only within a specific namespace","I need to retrieve cluster-scoped resources like Nodes without namespace confusion","I want to prevent accidental cross-namespace resource access"],"best_for":["Multi-tenant Kubernetes environments with namespace isolation","Teams enforcing namespace-level RBAC policies","Organizations building LLM tools with namespace-aware access control"],"limitations":["Scope enforcement relies on Kubernetes API server — MKP does not validate RBAC independently","No namespace filtering at MKP layer — LLM must specify namespace explicitly","Cluster-scoped resources cannot be filtered by namespace — all-or-nothing access"],"requires":["Kubernetes cluster with proper namespace isolation","RBAC policies limiting service account to intended namespaces"],"input_types":["Resource URI with explicit scope (namespaced or clustered)","Namespace parameter for namespaced resources"],"output_types":["Resources scoped to specified namespace or cluster"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_8","uri":"capability://tool.use.integration.subresource.access.status.scale.logs","name":"subresource access (status, scale, logs)","description":"Enables access to Kubernetes subresources like /status, /scale, and /logs by parsing subresource paths from resource URIs and routing requests to correct API endpoints. Implements subresource-specific request handling (e.g., logs as text stream, scale as numeric fields). Supports both read and write operations on subresources.","intents":["I want an LLM to read pod logs without accessing the full pod object","I need to scale a deployment by updating only the /scale subresource","I want to check deployment status without fetching the entire resource"],"best_for":["LLM-driven cluster troubleshooting and diagnostics","Automated scaling and resource management agents","Teams building LLM-powered cluster health monitoring"],"limitations":["Logs subresource returns raw text — no structured parsing or filtering","Scale subresource limited to numeric replicas — no advanced scaling policies (HPA, KEDA)","Status subresource read-only in most cases — LLM cannot modify status directly","Subresource availability varies by resource type — not all resources support all subresources"],"requires":["Kubernetes cluster 1.19+ with subresource support","Service account with permissions for specific subresources (logs, scale, status)"],"input_types":["Resource URI with subresource path (e.g., k8s://namespaced/default/v1/pods/my-pod/logs)","Subresource-specific parameters (tail lines for logs, replicas for scale)"],"output_types":["Text stream for logs","Numeric scale value","Status object with conditions and state"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mkp__cap_9","uri":"capability://safety.moderation.error.handling.and.kubernetes.api.error.propagation","name":"error handling and kubernetes api error propagation","description":"Captures Kubernetes API errors (404 NotFound, 403 Forbidden, 409 Conflict, etc.) and translates them into structured MCP error responses with detailed context. Implements retry logic for transient failures (connection timeouts, temporary API server unavailability). Provides human-readable error messages for LLM interpretation.","intents":["I want an LLM to understand why a resource creation failed (e.g., namespace doesn't exist)","I need clear error messages when RBAC denies access to a resource","I want the LLM to retry transient failures automatically"],"best_for":["LLM agents that need to handle Kubernetes errors gracefully","Teams building resilient cluster automation","Organizations debugging LLM-Kubernetes integration issues"],"limitations":["Retry logic limited to transient errors — permanent failures (403, 404) not retried","Error messages depend on Kubernetes API server verbosity — may be cryptic for complex validation failures","No structured error codes — LLM must parse error messages to determine failure type","Timeout handling uses fixed retry count — no exponential backoff configuration"],"requires":["Kubernetes cluster with proper error responses"],"input_types":["Any MCP tool call that may fail"],"output_types":["Structured MCP error response with HTTP status code and error message","Kubernetes API error details (reason, message, code)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Kubernetes cluster 1.19+ with accessible API server","Valid kubeconfig or in-cluster service account with read permissions","MCP-compatible LLM client (Claude, OpenAI with MCP support, or custom MCP consumer)","Service account with list permissions for target resource types","MCP-compatible LLM client","Acceptance of limited operation set (list, get, apply only)","Kubernetes cluster 1.19+ (1.22+ recommended for server-side apply)","Service account with create/update/patch permissions for target resource types","Valid YAML/JSON manifest as input","MCP-compatible LLM client (Claude 3.5+, OpenAI with MCP plugin, or custom MCP consumer)"],"failure_modes":["Subresource access limited to those exposed by Kubernetes API server (logs, status, scale)","No built-in caching — each request hits the Kubernetes API directly, adding ~50-200ms latency per call","Unstructured client returns generic map[string]interface{} — requires LLM to parse YAML/JSON structure","Pagination not exposed to MCP layer — returns all results in single response, may be slow for large clusters (1000+ resources)","No label selector or field selector support — filters only by namespace and resource type","API discovery calls add ~100-300ms overhead on first list operation per resource type","No delete operation — resources must be deleted via kubectl or other tools","No watch/streaming for resource changes — only point-in-time queries","No advanced filtering (label selectors, field selectors) — only namespace and resource type","No batch operations — each resource requires separate API call","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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":"2026-06-17T09:51:03.578Z","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=mkp","compare_url":"https://unfragile.ai/compare?artifact=mkp"}},"signature":"qf+CbmW6UsSZdNm7uqk26i2rkb2yZIrQfInf7YAq7RiL8jDRWDXDMTyVYlCIjEz8+Nh0q055zcyHsbEpHLCkBw==","signedAt":"2026-06-22T15:19:42.130Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mkp","artifact":"https://unfragile.ai/mkp","verify":"https://unfragile.ai/api/v1/verify?slug=mkp","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"}}