Kubernetes MCP Server
MCP ServerFreeManage Kubernetes clusters, pods, and deployments via MCP.
Capabilities11 decomposed
json-rpc mcp protocol bridging to kubernetes api
Medium confidenceImplements a standardized Model Context Protocol (MCP) server that translates JSON-RPC requests from MCP clients (Claude Desktop, etc.) into native Kubernetes API calls via the Go client library. The server handles protocol initialization handshakes where client and server exchange capability information, then routes incoming tool/resource/prompt requests to appropriate Kubernetes operations. Uses a stateless request-response pattern with no persistent connection state, allowing clients to discover available operations dynamically.
Implements MCP server in Go with native Kubernetes client library integration, providing direct cluster access without intermediate REST layers or cloud proxies. Uses MCP's resource/tool/prompt discovery mechanism to expose Kubernetes operations as discoverable capabilities rather than hardcoded endpoints.
Lighter-weight than cloud-based Kubernetes management platforms (no SaaS overhead) and more standardized than custom REST APIs, since it adheres to the MCP specification that any compatible client can consume.
multi-context kubernetes cluster switching
Medium confidenceExposes all configured Kubernetes contexts from the user's kubeconfig file as discoverable resources through the MCP protocol. The server reads the kubeconfig at startup and maintains a list of available contexts, allowing clients to query which clusters are accessible and switch between them dynamically. Each context maps to a separate Kubernetes client instance that targets that cluster's API server.
Automatically discovers and exposes all kubeconfig contexts as MCP resources without requiring manual configuration, allowing clients to dynamically query available clusters and switch between them within a single session.
More flexible than single-cluster tools (supports multi-cluster workflows) and more discoverable than kubectl context switching (clients can query available contexts programmatically).
kubernetes event retrieval and cluster activity monitoring
Medium confidenceProvides a tool to retrieve Kubernetes events from the cluster, which record significant occurrences like pod scheduling, image pulls, restarts, and errors. Queries the Kubernetes API for Event resources, optionally filtered by namespace, involved object, or time range. Events provide a timeline of cluster activity and are essential for troubleshooting. Returns structured event data with timestamps, reasons, messages, and involved resources.
Exposes Kubernetes Event API as a discoverable MCP tool, allowing clients to query cluster activity timeline without requiring kubectl or direct API access. Provides structured event data optimized for LLM analysis.
More accessible than kubectl describe (dedicated event tool) and more real-time than log aggregation (events capture cluster-level activity, not just pod logs).
pod log streaming and retrieval with filtering
Medium confidenceProvides a tool that retrieves logs from Kubernetes pods by querying the Kubernetes API's log endpoint. Supports filtering by pod name, namespace, container name, and optional line count limits. The implementation uses the Go Kubernetes client's PodLogOptions to construct log requests, then streams or buffers the response depending on the client's needs. Handles multi-container pods by allowing container selection.
Integrates with Kubernetes API's native log endpoint through the Go client library, supporting container selection and line limits without requiring kubectl binary or shell execution. Exposes logs as structured MCP tool output that LLMs can parse and analyze.
More direct than kubectl CLI (no subprocess overhead) and more LLM-friendly than raw log files (structured output format), though less feature-rich than dedicated log aggregation platforms like ELK or Datadog.
remote command execution in pod containers
Medium confidenceImplements pod exec functionality by establishing a WebSocket connection to the Kubernetes API's exec endpoint, allowing arbitrary commands to be executed inside running containers. Uses the Go client's Executor interface to handle stdin/stdout/stderr streams. Supports specifying target pod, namespace, container, and command with arguments. Handles connection setup, stream multiplexing, and error propagation back to the MCP client.
Uses Kubernetes API's WebSocket-based exec endpoint through the Go client library, handling stream multiplexing and connection lifecycle automatically. Exposes remote execution as a discoverable MCP tool rather than requiring kubectl binary or custom SSH setup.
More secure than SSH (uses Kubernetes RBAC and audit logging) and more discoverable than kubectl exec (available as a tool in any MCP client), though less interactive than a true shell session.
resource listing and filtering across namespaces
Medium confidenceProvides tools to list Kubernetes resources (pods, deployments, services, nodes, events) with optional filtering by namespace, label selectors, and field selectors. Uses the Go Kubernetes client's List operations with ListOptions to construct filtered queries. Returns structured JSON representations of resources with key metadata (name, namespace, status, age, etc.). Supports querying across all namespaces or specific namespaces.
Leverages Kubernetes API's native ListOptions with label and field selectors, allowing server-side filtering without fetching all resources. Returns structured JSON representations optimized for LLM consumption rather than raw YAML.
More efficient than kubectl list (server-side filtering reduces data transfer) and more discoverable than raw API calls (available as named tools in MCP), though less feature-rich than dedicated monitoring dashboards.
detailed resource inspection and description
Medium confidenceProvides a tool to retrieve detailed information about a specific Kubernetes resource (pod, deployment, service, etc.) by name and namespace. Uses the Go Kubernetes client's Get operation to fetch the full resource spec and status. Returns comprehensive metadata including labels, annotations, resource requests/limits, conditions, events, and other diagnostic information. Useful for deep-dive troubleshooting and understanding resource configuration.
Fetches complete resource definitions including all nested specs and status fields through the Kubernetes API, presenting them as structured JSON optimized for LLM analysis rather than human-readable YAML.
More comprehensive than kubectl describe (includes full spec and status in machine-readable format) and more direct than API documentation (actual current state, not template).
yaml configuration application and resource creation
Medium confidenceProvides a tool to apply Kubernetes YAML configurations to the cluster, supporting both resource creation and updates. Accepts YAML strings as input and uses the Go Kubernetes client's dynamic client to parse and apply resources. Supports multiple resources in a single YAML file (separated by '---'). Uses server-side apply semantics where available, allowing declarative configuration management. Handles resource versioning and API group resolution automatically.
Uses Kubernetes dynamic client to parse and apply arbitrary YAML without requiring resource-specific knowledge, supporting server-side apply semantics for declarative configuration management. Handles multi-resource YAML files and API group resolution automatically.
More flexible than kubectl apply (no binary dependency) and more discoverable than raw API calls (available as named tool in MCP), though less safe than GitOps workflows (no version control or approval gates).
interactive prompt templates for common kubernetes queries
Medium confidenceExposes pre-built prompt templates through the MCP Prompts system that guide users through common Kubernetes operations like listing pods or namespaces. These prompts are discoverable by MCP clients and can be invoked to generate structured queries. The prompts provide context-aware suggestions and parameter hints, making it easier for non-expert users to construct valid Kubernetes queries. Implemented as static prompt definitions that map to underlying tools.
Implements MCP Prompts system to expose pre-built Kubernetes query templates as discoverable operations, allowing clients to guide users through common operations without requiring Kubernetes expertise.
More discoverable than documentation (available in MCP client UI) and more guided than raw tool invocation (provides context and parameter hints).
namespace enumeration and context-aware defaults
Medium confidenceProvides tools to list all namespaces in the current Kubernetes context and retrieve the default namespace for the active context. Queries the Kubernetes API for all Namespace resources and extracts the default namespace from the kubeconfig context configuration. Allows clients to discover available namespaces and understand the current operational scope without manual configuration.
Combines Kubernetes API namespace listing with kubeconfig context defaults, providing both cluster-wide namespace discovery and context-specific defaults in a single operation.
More context-aware than raw namespace listing (includes default namespace from kubeconfig) and more discoverable than kubectl commands (available as named tools).
cluster node inventory and resource availability checking
Medium confidenceProvides a tool to list all nodes in the Kubernetes cluster with their resource capacity, allocatable resources, and current utilization status. Queries the Kubernetes API for Node resources and extracts capacity, allocatable, and status information. Allows operators to understand cluster resource availability and identify resource constraints or node issues. Returns structured data including CPU, memory, storage, and pod count information.
Aggregates node capacity, allocatable, and status information from the Kubernetes API into a structured format optimized for capacity planning and resource constraint identification.
More comprehensive than kubectl top (includes capacity and allocatable data, not just current usage) and more discoverable than raw API calls (available as named tool).
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Kubernetes MCP Server, ranked by overlap. Discovered automatically through the match graph.
mcp-k8s-go
** - Golang-based Kubernetes MCP Server. Built to be extensible.
mcp-server-kubernetes
MCP server for interacting with Kubernetes clusters via kubectl
kubernetes-mcp-server
Model Context Protocol (MCP) server for Kubernetes and OpenShift
mcp-server-kubernetes
MCP server for interacting with Kubernetes clusters via kubectl
Metoro
** - Query and interact with kubernetes environments monitored by Metoro
kubernetes-mcp-server
Model Context Protocol (MCP) server for Kubernetes and OpenShift
Best For
- ✓DevOps teams integrating Kubernetes management into Claude Desktop or other MCP clients
- ✓Platform engineers building LLM-powered infrastructure automation tools
- ✓Organizations standardizing on MCP for tool discovery and invocation across multiple services
- ✓Multi-cluster operators managing dev/staging/production environments
- ✓Platform teams providing cluster access to multiple teams through a single MCP interface
- ✓DevOps engineers who need to quickly switch contexts without leaving their LLM chat interface
- ✓DevOps engineers troubleshooting pod failures and understanding cluster activity
- ✓On-call engineers investigating incidents through event timelines
Known Limitations
- ⚠No built-in authentication beyond kubeconfig — relies entirely on kubectl credentials and RBAC policies already configured
- ⚠Stateless design means no transaction support or rollback capabilities across multiple operations
- ⚠JSON-RPC protocol adds serialization overhead compared to direct gRPC calls to Kubernetes API
- ⚠No connection pooling or request batching — each MCP request triggers a separate Kubernetes API call
- ⚠Context switching is stateless per request — no session persistence, so each operation must specify target context
- ⚠No context validation or connectivity checking at startup — invalid contexts are only discovered when operations fail
Requirements
Input / Output
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About
Community MCP server for Kubernetes cluster management. Exposes tools for listing pods, deployments, and services, viewing logs, describing resources, and applying YAML configurations.
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