What can kubernetes-mcp-server do?

kubernetes cluster introspection via mcp protocol, namespace-scoped resource filtering and listing, openshift-specific resource querying, pod and container status inspection, deployment and replicaset configuration retrieval, service and endpoint discovery, configmap and secret metadata inspection, container log streaming and retrieval, node and cluster resource inspection, custom resource definition (crd) querying, mcp tool registration and schema exposure

kubernetes-mcp-server

MCP ServerFree

Model Context Protocol (MCP) server for Kubernetes and OpenShift

Open Source

/ 100

11 capabilities

Capabilities11 decomposed

kubernetes cluster introspection via mcp protocol

Medium confidence

Exposes Kubernetes API resources (pods, deployments, services, configmaps, secrets, etc.) as MCP tools that LLM agents can invoke. Uses the Kubernetes client library to authenticate against kubeconfig and translate kubectl-equivalent queries into structured resource listings with full metadata, enabling agents to inspect cluster state without direct kubectl access.

Solves for

Query current pod status and resource usage across namespaces from an LLM agentRetrieve deployment configurations and replica counts to diagnose scaling issuesList available services and their endpoints for network troubleshootingInspect ConfigMaps and Secrets metadata (without exposing sensitive values) for configuration auditing

Best for

DevOps teams building AI-assisted cluster management agents

Platform engineers integrating Kubernetes observability into LLM workflows

SREs automating incident response with context-aware AI assistants

Requires

Node.js 16+

Valid kubeconfig file with cluster credentials

kubectl CLI installed (for authentication context)

Limitations

Requires valid kubeconfig with appropriate RBAC permissions — cannot escalate privileges beyond authenticated user

Returns metadata only; does not stream real-time events or watch resources for changes

No built-in caching — each query hits the Kubernetes API directly, adding latency for large clusters

What makes it unique

Bridges Kubernetes API directly into MCP protocol, allowing LLM agents to query cluster state through standardized tool-calling interface rather than shelling out to kubectl or managing raw API calls

vs alternatives

Simpler than building custom Kubernetes API clients in agent code; more structured than kubectl JSON parsing; integrates natively with Claude and other MCP-compatible LLMs without wrapper scripts

namespace-scoped resource filtering and listing

Medium confidence

Implements namespace-aware filtering for Kubernetes resources, allowing agents to query resources within specific namespaces or across all namespaces. Uses the Kubernetes client's namespace parameter to scope API calls and returns filtered lists with namespace context preserved in metadata, enabling multi-tenant cluster operations.

Solves for

List all pods in a specific namespace to check application healthQuery resources across all namespaces to audit cluster-wide configurationsFilter deployments by namespace to isolate team-specific infrastructureRetrieve namespace-scoped secrets for environment-specific credential management

Best for

Multi-tenant Kubernetes clusters with namespace isolation

Teams managing separate environments (dev, staging, prod) via namespaces

Agents requiring namespace-scoped RBAC enforcement

Requires

Kubernetes cluster with namespace support

RBAC role allowing 'list' and 'get' verbs on target resources

kubeconfig with namespace context configured

Limitations

Respects RBAC — agent can only list namespaces and resources the authenticated user has permission to access

No namespace creation/deletion — read-only for namespace enumeration

Cross-namespace queries require explicit permission grants in RBAC policies

What makes it unique

Implements namespace-scoped queries as first-class MCP tools rather than requiring agents to manually construct namespace filters, with RBAC enforcement built into the query layer

vs alternatives

More granular than kubectl's default namespace switching; enforces RBAC at query time rather than relying on client-side filtering; integrates namespace context directly into MCP tool signatures

openshift-specific resource querying

Medium confidence

Extends Kubernetes resource querying to support OpenShift-specific resources (Routes, Projects, DeploymentConfigs, ImageStreams, etc.) using the same MCP tool interface. Detects OpenShift cluster and exposes OpenShift API groups alongside standard Kubernetes resources, enabling agents to manage OpenShift deployments with the same tool set.

Solves for

Query OpenShift Routes to understand external service exposure and TLS configurationInspect DeploymentConfigs and ImageStreams for OpenShift-native application deploymentsList OpenShift Projects to understand multi-tenant organizationRetrieve OpenShift-specific metadata for compliance and configuration auditing

Best for

Teams managing OpenShift clusters with AI agents

Hybrid Kubernetes/OpenShift environments requiring unified tooling

OpenShift operators and platform teams automating cluster management

Requires

OpenShift cluster (3.11+) or vanilla Kubernetes cluster

RBAC permission to access OpenShift resources (routes, projects, deploymentconfigs, imagestreams)

kubeconfig with OpenShift cluster context

Limitations

OpenShift-specific features only available on OpenShift clusters — gracefully degrades on vanilla Kubernetes

DeploymentConfig deprecation — newer OpenShift versions prefer Deployments

ImageStream integration requires OpenShift image registry access

What makes it unique

Detects OpenShift cluster and automatically exposes OpenShift-specific resources (Routes, Projects, DeploymentConfigs) through the same MCP tool interface as Kubernetes resources, enabling unified agent tooling across both platforms

vs alternatives

Single tool set for Kubernetes and OpenShift; automatic platform detection; no separate OpenShift-specific agent configuration required; cleaner than maintaining separate Kubernetes and OpenShift tool implementations

pod and container status inspection

Medium confidence

Provides detailed pod status including container states, restart counts, resource requests/limits, and node assignments. Queries the Kubernetes API for pod metadata and status subresources, returning structured data about container readiness, phase transitions, and resource allocation to help agents diagnose pod health and performance issues.

Solves for

Check if a pod is running and ready to receive trafficIdentify pods that are crashing or in CrashLoopBackOff stateRetrieve container resource requests and limits to diagnose OOM or CPU throttlingFind which node a pod is scheduled on for infrastructure debugging

Best for

Incident response agents diagnosing pod failures

Automated health-check systems monitoring application readiness

Cost optimization tools analyzing resource allocation vs actual usage

Requires

Kubernetes cluster with pods deployed

RBAC permission to 'get' pods and 'get' pods/status

Valid kubeconfig authentication

Limitations

Does not stream logs — separate capability required for container logs

Status reflects point-in-time snapshot; does not track historical state changes

Resource metrics (CPU, memory actual usage) require metrics-server — status only shows requests/limits

What makes it unique

Exposes Kubernetes pod status subresource through MCP, giving agents structured access to container state machines (Waiting, Running, Terminated) and condition arrays rather than requiring log parsing or raw API calls

vs alternatives

More reliable than parsing kubectl output; includes structured condition data that kubectl hides; integrates pod status directly into agent decision-making without intermediate parsing layers

deployment and replicaset configuration retrieval

Medium confidence

Queries deployment and ReplicaSet resources to return pod templates, replica counts, update strategies, and selector labels. Uses the Kubernetes API to fetch spec and status fields, enabling agents to understand scaling configuration, image versions, and rollout state without inspecting individual pods.

Solves for

Check current and desired replica counts to diagnose scaling mismatchesRetrieve pod template specs to verify image versions and environment variablesInspect update strategy (RollingUpdate, Recreate) to understand deployment behaviorList ReplicaSets associated with a deployment to track rollout history

Best for

Deployment automation agents managing application scaling

Configuration auditing systems verifying image versions across environments

Rollout analysis tools tracking deployment progress and health

Requires

Kubernetes cluster with Deployment or ReplicaSet resources

RBAC permission to 'get' and 'list' deployments and replicasets

kubeconfig with cluster access

Limitations

Does not trigger deployments or scaling — read-only capability

Rollout history limited to ReplicaSets in cluster; does not access external registries for image metadata

No support for Helm values or templating — returns raw Kubernetes objects only

What makes it unique

Exposes deployment and ReplicaSet specs as MCP tools with structured access to pod templates and scaling configuration, allowing agents to reason about deployment intent without kubectl templating or manual YAML parsing

vs alternatives

Cleaner than kubectl get -o json piped through jq; includes ReplicaSet history context; integrates deployment configuration directly into agent reasoning about scaling and updates

service and endpoint discovery

Medium confidence

Retrieves Service resources and their associated Endpoints, exposing cluster DNS names, port mappings, and backend pod addresses. Queries the Kubernetes API for Service specs and Endpoint subresources, enabling agents to understand network topology and service routing without manual DNS lookups or port-forwarding.

Solves for

Discover available services and their DNS names for inter-service communicationRetrieve service port mappings to understand load balancing configurationList endpoints backing a service to verify pod connectivityIdentify services with no endpoints (broken routing) for troubleshooting

Best for

Network debugging agents diagnosing service connectivity issues

Service mesh integration tools mapping cluster topology

Load balancing configuration auditors verifying endpoint health

Requires

Kubernetes cluster with Service resources

RBAC permission to 'get' and 'list' services and endpoints

kubeconfig with cluster access

Limitations

Does not perform actual connectivity tests — returns endpoint addresses only

Endpoint data is point-in-time; does not track endpoint churn or health check failures

No support for external services or DNS CNAME resolution

What makes it unique

Combines Service and Endpoint queries into a single MCP tool, giving agents unified visibility into cluster DNS and routing without separate API calls or manual endpoint enumeration

vs alternatives

More direct than kubectl service discovery commands; includes endpoint data in single query; integrates network topology directly into agent reasoning about service connectivity

configmap and secret metadata inspection

Medium confidence

Retrieves ConfigMap and Secret resource metadata including keys, data size, and creation timestamps, without exposing sensitive values. Uses the Kubernetes API to fetch resource metadata and key lists, enabling agents to audit configuration and secret usage without accessing plaintext credentials or large data payloads.

Solves for

List ConfigMap keys to verify configuration is present without reading valuesAudit Secret resources to identify unused or stale credentialsCheck ConfigMap size and key count to diagnose configuration bloatVerify ConfigMap and Secret ownership and creation timestamps for compliance

Best for

Security auditing agents verifying secret management practices

Configuration management tools tracking ConfigMap changes

Compliance systems ensuring proper secret lifecycle management

Requires

Kubernetes cluster with ConfigMap or Secret resources

RBAC permission to 'get' and 'list' configmaps and secrets

kubeconfig with cluster access

Limitations

Does not return ConfigMap or Secret values — metadata only for security

Cannot detect if secrets are actually used by pods — requires separate pod inspection

No change tracking or audit log access — returns current state only

What makes it unique

Implements metadata-only inspection of Secrets and ConfigMaps through MCP, preventing accidental exposure of sensitive data while still allowing agents to audit configuration and secret usage patterns

vs alternatives

Safer than kubectl get secrets -o json which exposes base64-encoded values; provides structured metadata for auditing without requiring custom RBAC policies; integrates security-conscious inspection into agent workflows

container log streaming and retrieval

Medium confidence

Fetches container logs from pods using the Kubernetes API logs endpoint, supporting tail limits, timestamp filtering, and multi-container selection. Streams or buffers log data and returns structured output with container context, enabling agents to diagnose application issues without kubectl access or log aggregation systems.

Solves for

Retrieve recent logs from a failing pod to diagnose crash causesTail logs from a specific container to monitor real-time application behaviorFetch logs with timestamps to correlate events across multiple containersGet previous logs from a restarted container to understand failure history

Best for

Incident response agents diagnosing application failures

Automated troubleshooting systems analyzing error patterns

Development teams debugging application behavior in cluster

Requires

Kubernetes cluster with running or recently-terminated pods

RBAC permission to 'get' pods/log

kubeconfig with cluster access

Limitations

Limited to logs stored on node — does not access external log aggregation systems

Log retention depends on container runtime and node disk space — old logs may be unavailable

Large log streams may timeout or consume significant memory — requires pagination for high-volume logs

What makes it unique

Exposes Kubernetes pod logs API through MCP with structured container context and filtering options, allowing agents to retrieve and analyze logs without kubectl or log aggregation platform access

vs alternatives

More direct than kubectl logs with piping; supports multi-container context; integrates log retrieval into agent decision-making without external log platform dependencies

node and cluster resource inspection

Medium confidence

Queries Node resources to retrieve capacity, allocatable resources, and node conditions (Ready, MemoryPressure, DiskPressure, etc.). Uses the Kubernetes API to fetch node metadata and status, enabling agents to understand cluster capacity and node health without metrics-server or custom monitoring.

Solves for

Check node capacity and allocatable resources to diagnose scheduling failuresIdentify nodes with pressure conditions (memory, disk) affecting pod placementVerify node readiness and taints/tolerations for cluster health assessmentRetrieve node labels and annotations for infrastructure topology understanding

Best for

Cluster capacity planning agents optimizing resource allocation

Node health monitoring systems detecting infrastructure issues

Scheduling analysis tools diagnosing pod placement failures

Requires

Kubernetes cluster with nodes

RBAC permission to 'get' and 'list' nodes

kubeconfig with cluster access

Limitations

Returns capacity and allocatable resources only — does not include actual usage metrics

Node conditions are point-in-time snapshots — does not track condition history

Cannot modify node taints, labels, or cordoning through this capability

What makes it unique

Exposes Kubernetes Node API through MCP with structured access to capacity, conditions, and taints, enabling agents to reason about cluster infrastructure without metrics-server or custom monitoring tools

vs alternatives

Simpler than querying metrics-server; includes node conditions and taint data; integrates infrastructure context directly into agent capacity planning and scheduling analysis

custom resource definition (crd) querying

Medium confidence

Supports querying custom resources defined via CustomResourceDefinitions (CRDs), allowing agents to inspect application-specific Kubernetes objects beyond built-in resource types. Uses the Kubernetes API's dynamic client to handle arbitrary CRD schemas, returning structured data for custom resources with full metadata and spec access.

Solves for

Query custom resources (e.g., Istio VirtualServices, Prometheus Rules) for application configurationInspect CRD instances to verify custom resource state and complianceList custom resources across namespaces to audit application-specific configurationsRetrieve CRD metadata to understand custom resource ownership and relationships

Best for

Agents managing service mesh or observability platforms with CRDs

Teams using custom operators with application-specific resource types

Multi-cluster management systems auditing custom resource configurations

Requires

Kubernetes cluster with CRDs installed

RBAC permission to 'get' and 'list' custom resources

kubeconfig with cluster access

Limitations

Requires knowledge of CRD group, version, and kind — no automatic CRD discovery

Schema validation depends on CRD definition — no built-in schema enforcement

Cannot create, update, or delete custom resources through this capability

What makes it unique

Uses Kubernetes dynamic client to support arbitrary CRDs without hardcoding resource types, enabling agents to query application-specific resources (service mesh, operators, etc.) through a unified MCP interface

vs alternatives

More flexible than hardcoded resource types; supports any CRD without code changes; integrates custom resource querying into agent workflows without requiring separate API clients per CRD

mcp tool registration and schema exposure

Medium confidence

Implements the Model Context Protocol by registering Kubernetes query capabilities as MCP tools with JSON Schema definitions, enabling LLM clients to discover and invoke Kubernetes operations through standardized tool-calling interfaces. Uses MCP server SDK to define tool schemas, handle tool invocations, and return structured results compatible with Claude and other MCP-aware LLMs.

Solves for

Enable Claude and other LLMs to discover available Kubernetes operations via MCP tool listingInvoke Kubernetes queries from LLM agents using standardized tool-calling syntaxProvide structured tool schemas so LLMs understand required and optional parametersReturn tool results in formats compatible with LLM context windows and reasoning

Best for

Teams building AI agents that manage Kubernetes clusters

LLM application developers integrating cluster operations into agent workflows

Organizations standardizing on MCP for tool integration across multiple systems

Requires

Node.js 16+

MCP server SDK (@modelcontextprotocol/sdk)

MCP-compatible LLM client or application

Limitations

Requires MCP-compatible LLM client (Claude, custom implementations) — not compatible with standard OpenAI API

Tool invocation latency includes MCP protocol overhead (~50-200ms per call)

Schema definitions must be manually maintained as Kubernetes API evolves

What makes it unique

Implements full MCP server specification for Kubernetes, exposing cluster operations as discoverable tools with JSON Schema definitions rather than requiring agents to construct raw API calls or kubectl commands

vs alternatives

Standardized MCP protocol enables interoperability with multiple LLM clients; structured schemas enable LLMs to understand tool parameters without documentation; cleaner than custom function-calling implementations

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 Server23

mcp-k8s-go

** - Golang-based Kubernetes MCP Server. Built to be extensible.

kubernetes resource listing with namespace and type filteringdetailed kubernetes resource inspection with full specification retrievalkubernetes event retrieval and cluster event monitoringnamespace enumeration and context-aware namespace selection

4 shared capabilities

MCP Server40

mcp-server-kubernetes

MCP server for interacting with Kubernetes clusters via kubectl

kubernetes resource querying and inspectionnamespace isolation and resource scopingcluster event and status monitoring

3 shared capabilities

MCP Server38

kubernetes-mcp-server

Model Context Protocol (MCP) server for Kubernetes and OpenShift

kubernetes-cluster-resource-introspectionopenshift-specific-resource-managementcluster-context-and-namespace-scoping

3 shared capabilities

MCP Server21

Metoro

** - Query and interact with kubernetes environments monitored by Metoro

kubernetes-cluster-state-querying-via-mcpkubernetes-namespace-and-workload-filteringmcp-resource-schema-mapping-for-kubernetes-objects

3 shared capabilities

MCP Server46

Kubernetes MCP Server

Manage Kubernetes clusters, pods, and deployments via MCP.

resource listing and filtering across namespacesdetailed resource inspection and description

2 shared capabilities

MCP Server24

Kubernetes

** - Connect to Kubernetes cluster and manage pods, deployments, services.

cluster-wide resource discovery and introspectionresource query and filtering with structured output

2 shared capabilities

Best For

✓DevOps teams building AI-assisted cluster management agents
✓Platform engineers integrating Kubernetes observability into LLM workflows
✓SREs automating incident response with context-aware AI assistants
✓Multi-tenant Kubernetes clusters with namespace isolation
✓Teams managing separate environments (dev, staging, prod) via namespaces
✓Agents requiring namespace-scoped RBAC enforcement
✓Teams managing OpenShift clusters with AI agents
✓Hybrid Kubernetes/OpenShift environments requiring unified tooling

Known Limitations

⚠Requires valid kubeconfig with appropriate RBAC permissions — cannot escalate privileges beyond authenticated user
⚠Returns metadata only; does not stream real-time events or watch resources for changes
⚠No built-in caching — each query hits the Kubernetes API directly, adding latency for large clusters
⚠Limited to single cluster per MCP server instance — multi-cluster requires multiple server instances
⚠Respects RBAC — agent can only list namespaces and resources the authenticated user has permission to access
⚠No namespace creation/deletion — read-only for namespace enumeration

Requirements

Node.js 16+Valid kubeconfig file with cluster credentialskubectl CLI installed (for authentication context)RBAC permissions matching the authenticated user's roleKubernetes cluster with namespace supportRBAC role allowing 'list' and 'get' verbs on target resourceskubeconfig with namespace context configuredOpenShift cluster (3.11+) or vanilla Kubernetes cluster

Input / Output

Accepts: resource type (string: 'pods', 'deployments', 'services', etc.), namespace (string, optional — defaults to 'default'), label selectors (string, optional — for filtering), namespace name (string, optional), resource type (string), label selector (string, optional), OpenShift resource type (string: 'route', 'project', 'deploymentconfig', 'imagestream'), resource name (string, optional), namespace/project (string, optional), pod name (string), namespace (string, optional), deployment name (string), resource type (string: 'deployment' or 'replicaset'), service name (string), resource type (string: 'service' or 'endpoints'), resource name (string), resource type (string: 'configmap' or 'secret'), container name (string, optional — defaults to first container), tail lines (integer, optional — default 100), timestamps (boolean, optional — include timestamps in output), previous (boolean, optional — fetch logs from previous container instance), node name (string, optional — lists all nodes if omitted), CRD group (string, e.g., 'networking.istio.io'), CRD version (string, e.g., 'v1beta1'), CRD kind (string, e.g., 'VirtualService'), MCP tool invocation with parameters matching tool schema, JSON-formatted tool arguments

Produces: structured JSON with resource metadata, resource lists with status fields, error messages for permission or connectivity issues, filtered resource list with namespace metadata, empty list if no resources match criteria, permission denied error if RBAC denies access, OpenShift resource spec and status, route hostname and TLS configuration, ImageStream tags and image references, DeploymentConfig triggers and rollout configuration, structured JSON with pod phase, conditions, container states, container restart count and last state information, resource requests/limits per container, deployment spec with pod template and update strategy, status with current and desired replica counts, list of associated ReplicaSets with revision numbers, service spec with ports, selectors, and cluster IP, endpoint addresses and ports for backend pods, service type (ClusterIP, NodePort, LoadBalancer, ExternalName), resource metadata with creation timestamp and owner references, list of keys without values, data size and key count, raw log lines with optional timestamps, error messages if pod or container not found, truncation warnings if log stream exceeds size limits, node capacity and allocatable resources, node conditions and status, taints, labels, and annotations, list of all nodes with summary data, custom resource spec and status, custom resource metadata and ownership, list of custom resources matching criteria, MCP tool result with structured JSON response, error messages in MCP-compatible format, tool schemas in JSON Schema format for client discovery

UnfragileRank

Adoption39%(30% weight)

Quality22%(25% weight)

Ecosystem50%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

11 capabilities

Visit kubernetes-mcp-server→

Package Details

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Registry

0.0.60

Version

4,429

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Model Context Protocol (MCP) server for Kubernetes and OpenShift

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Capabilities11 decomposed

kubernetes cluster introspection via mcp protocol

Medium confidence

Solves for

Best for

DevOps teams building AI-assisted cluster management agents

Platform engineers integrating Kubernetes observability into LLM workflows

SREs automating incident response with context-aware AI assistants

Requires

Node.js 16+

Valid kubeconfig file with cluster credentials

kubectl CLI installed (for authentication context)

Limitations

Requires valid kubeconfig with appropriate RBAC permissions — cannot escalate privileges beyond authenticated user

Returns metadata only; does not stream real-time events or watch resources for changes

No built-in caching — each query hits the Kubernetes API directly, adding latency for large clusters

What makes it unique

Bridges Kubernetes API directly into MCP protocol, allowing LLM agents to query cluster state through standardized tool-calling interface rather than shelling out to kubectl or managing raw API calls

vs alternatives

Simpler than building custom Kubernetes API clients in agent code; more structured than kubectl JSON parsing; integrates natively with Claude and other MCP-compatible LLMs without wrapper scripts

namespace-scoped resource filtering and listing

Medium confidence

Solves for

Best for

Multi-tenant Kubernetes clusters with namespace isolation

Teams managing separate environments (dev, staging, prod) via namespaces

Agents requiring namespace-scoped RBAC enforcement

Requires

Kubernetes cluster with namespace support

RBAC role allowing 'list' and 'get' verbs on target resources

kubeconfig with namespace context configured

Limitations

Respects RBAC — agent can only list namespaces and resources the authenticated user has permission to access

No namespace creation/deletion — read-only for namespace enumeration

Cross-namespace queries require explicit permission grants in RBAC policies

What makes it unique

Implements namespace-scoped queries as first-class MCP tools rather than requiring agents to manually construct namespace filters, with RBAC enforcement built into the query layer

vs alternatives

More granular than kubectl's default namespace switching; enforces RBAC at query time rather than relying on client-side filtering; integrates namespace context directly into MCP tool signatures

openshift-specific resource querying

Medium confidence

Solves for

Best for

Teams managing OpenShift clusters with AI agents

Hybrid Kubernetes/OpenShift environments requiring unified tooling

OpenShift operators and platform teams automating cluster management

Requires

OpenShift cluster (3.11+) or vanilla Kubernetes cluster

RBAC permission to access OpenShift resources (routes, projects, deploymentconfigs, imagestreams)

kubeconfig with OpenShift cluster context

Limitations

OpenShift-specific features only available on OpenShift clusters — gracefully degrades on vanilla Kubernetes

DeploymentConfig deprecation — newer OpenShift versions prefer Deployments

ImageStream integration requires OpenShift image registry access

What makes it unique

vs alternatives

pod and container status inspection

Medium confidence

Solves for

Best for

Incident response agents diagnosing pod failures

Automated health-check systems monitoring application readiness

Cost optimization tools analyzing resource allocation vs actual usage

Requires

Kubernetes cluster with pods deployed

RBAC permission to 'get' pods and 'get' pods/status

Valid kubeconfig authentication

Limitations

Does not stream logs — separate capability required for container logs

Status reflects point-in-time snapshot; does not track historical state changes

Resource metrics (CPU, memory actual usage) require metrics-server — status only shows requests/limits

What makes it unique

vs alternatives

More reliable than parsing kubectl output; includes structured condition data that kubectl hides; integrates pod status directly into agent decision-making without intermediate parsing layers

deployment and replicaset configuration retrieval

Medium confidence

Solves for

Best for

Deployment automation agents managing application scaling

Configuration auditing systems verifying image versions across environments

Rollout analysis tools tracking deployment progress and health

Requires

Kubernetes cluster with Deployment or ReplicaSet resources

RBAC permission to 'get' and 'list' deployments and replicasets

kubeconfig with cluster access

Limitations

Does not trigger deployments or scaling — read-only capability

Rollout history limited to ReplicaSets in cluster; does not access external registries for image metadata

No support for Helm values or templating — returns raw Kubernetes objects only

What makes it unique

vs alternatives

Cleaner than kubectl get -o json piped through jq; includes ReplicaSet history context; integrates deployment configuration directly into agent reasoning about scaling and updates

service and endpoint discovery

Medium confidence

Solves for

Best for

Network debugging agents diagnosing service connectivity issues

Service mesh integration tools mapping cluster topology

Load balancing configuration auditors verifying endpoint health

Requires

Kubernetes cluster with Service resources

RBAC permission to 'get' and 'list' services and endpoints

kubeconfig with cluster access

Limitations

Does not perform actual connectivity tests — returns endpoint addresses only

Endpoint data is point-in-time; does not track endpoint churn or health check failures

No support for external services or DNS CNAME resolution

What makes it unique

Combines Service and Endpoint queries into a single MCP tool, giving agents unified visibility into cluster DNS and routing without separate API calls or manual endpoint enumeration

vs alternatives

More direct than kubectl service discovery commands; includes endpoint data in single query; integrates network topology directly into agent reasoning about service connectivity

configmap and secret metadata inspection

Medium confidence

Solves for

Best for

Security auditing agents verifying secret management practices

Configuration management tools tracking ConfigMap changes

Compliance systems ensuring proper secret lifecycle management

Requires

Kubernetes cluster with ConfigMap or Secret resources

RBAC permission to 'get' and 'list' configmaps and secrets

kubeconfig with cluster access

Limitations

Does not return ConfigMap or Secret values — metadata only for security

Cannot detect if secrets are actually used by pods — requires separate pod inspection

No change tracking or audit log access — returns current state only

What makes it unique

vs alternatives

container log streaming and retrieval

Medium confidence

Solves for

Best for

Incident response agents diagnosing application failures

Automated troubleshooting systems analyzing error patterns

Development teams debugging application behavior in cluster

Requires

Kubernetes cluster with running or recently-terminated pods

RBAC permission to 'get' pods/log

kubeconfig with cluster access

Limitations

Limited to logs stored on node — does not access external log aggregation systems

Log retention depends on container runtime and node disk space — old logs may be unavailable

Large log streams may timeout or consume significant memory — requires pagination for high-volume logs

What makes it unique

Exposes Kubernetes pod logs API through MCP with structured container context and filtering options, allowing agents to retrieve and analyze logs without kubectl or log aggregation platform access

vs alternatives

More direct than kubectl logs with piping; supports multi-container context; integrates log retrieval into agent decision-making without external log platform dependencies

node and cluster resource inspection

Medium confidence

Solves for

Best for

Cluster capacity planning agents optimizing resource allocation

Node health monitoring systems detecting infrastructure issues

Scheduling analysis tools diagnosing pod placement failures

Requires

Kubernetes cluster with nodes

RBAC permission to 'get' and 'list' nodes

kubeconfig with cluster access

Limitations

Returns capacity and allocatable resources only — does not include actual usage metrics

Node conditions are point-in-time snapshots — does not track condition history

Cannot modify node taints, labels, or cordoning through this capability

What makes it unique

vs alternatives

Simpler than querying metrics-server; includes node conditions and taint data; integrates infrastructure context directly into agent capacity planning and scheduling analysis

custom resource definition (crd) querying

Medium confidence

Solves for

Best for

Agents managing service mesh or observability platforms with CRDs

Teams using custom operators with application-specific resource types

Multi-cluster management systems auditing custom resource configurations

Requires

Kubernetes cluster with CRDs installed

RBAC permission to 'get' and 'list' custom resources

kubeconfig with cluster access

Limitations

Requires knowledge of CRD group, version, and kind — no automatic CRD discovery

Schema validation depends on CRD definition — no built-in schema enforcement

Cannot create, update, or delete custom resources through this capability

What makes it unique

vs alternatives

More flexible than hardcoded resource types; supports any CRD without code changes; integrates custom resource querying into agent workflows without requiring separate API clients per CRD

mcp tool registration and schema exposure

Medium confidence

Solves for

Best for

Teams building AI agents that manage Kubernetes clusters

LLM application developers integrating cluster operations into agent workflows

Organizations standardizing on MCP for tool integration across multiple systems

Requires

Node.js 16+

MCP server SDK (@modelcontextprotocol/sdk)

MCP-compatible LLM client or application

Limitations

Requires MCP-compatible LLM client (Claude, custom implementations) — not compatible with standard OpenAI API

Tool invocation latency includes MCP protocol overhead (~50-200ms per call)

Schema definitions must be manually maintained as Kubernetes API evolves

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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kubernetes-mcp-server

Capabilities11 decomposed

kubernetes cluster introspection via mcp protocol

namespace-scoped resource filtering and listing

openshift-specific resource querying

pod and container status inspection

deployment and replicaset configuration retrieval

service and endpoint discovery

configmap and secret metadata inspection

container log streaming and retrieval

node and cluster resource inspection

custom resource definition (crd) querying

mcp tool registration and schema exposure

Related Artifactssharing capabilities

mcp-k8s-go

mcp-server-kubernetes

kubernetes-mcp-server

Metoro

Kubernetes MCP Server

Kubernetes

Best For

Known Limitations

Requirements

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kubernetes-mcp-server

Capabilities11 decomposed

kubernetes cluster introspection via mcp protocol

namespace-scoped resource filtering and listing

openshift-specific resource querying

pod and container status inspection

deployment and replicaset configuration retrieval

service and endpoint discovery

configmap and secret metadata inspection

container log streaming and retrieval

node and cluster resource inspection

custom resource definition (crd) querying

mcp tool registration and schema exposure

Related Artifactssharing capabilities

mcp-k8s-go

mcp-server-kubernetes

kubernetes-mcp-server

Metoro

Kubernetes MCP Server

Kubernetes

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to kubernetes-mcp-server

Are you the builder of kubernetes-mcp-server?

Get the weekly brief

Data Sources