tencentcloud-tke

name	tencentcloud-tke
description	Manage Tencent Cloud TKE (Tencent Kubernetes Engine) clusters and workloads. Use when the user asks to: list clusters, check cluster / node health, list pods or services, scale a Deployment, do a rolling restart, fetch kubeconfig, view recent K8s events, manage node pools. Combines the official tencentcloud-sdk-python TKE client (cluster metadata) with kubectl for in-cluster operations.
license	Apache-2.0
metadata	{"author":"acedatacloud","version":"1.0"}
connections	["tencentcloud"]

Tencent Cloud TKE (Kubernetes)

Manage TKE clusters and the workloads inside them.

Setup: See tencentcloud authentication. Cluster discovery and kubeconfig retrieval go through the SDK; everything inside the cluster (pods, services, scale, restart) goes through kubectl against the kubeconfig we fetch.

CLI (preferred)

The skill ships scripts/tke.py — wraps cluster discovery, kubeconfig retrieval, and the most common in-cluster operations.

# $SKILL_DIR can point at another skill loaded this turn — anchor on our own
# script (re-run this at the top of every fresh-shell Bash block).
TKE="$SKILL_DIR/scripts/tke.py"; [ -f "$TKE" ] || TKE=$(find /tmp -maxdepth 8 -path '*/skills/*/scripts/tke.py' 2>/dev/null | head -1)
[ -f "$TKE" ] || { echo "tencentcloud-tke script not found (SKILL_DIR=$SKILL_DIR)" >&2; exit 1; }

python3 $TKE clusters                                              # list clusters
python3 $TKE cluster cls-xxxxxxxx                                  # one cluster's details
python3 $TKE nodes cls-xxxxxxxx
python3 $TKE pools cls-xxxxxxxx                                    # node pools
python3 $TKE kubeconfig cls-xxxxxxxx --save ~/.kube/config-tke     # write kubeconfig
python3 $TKE workloads cls-xxxxxxxx -n my-namespace
python3 $TKE pods cls-xxxxxxxx -n my-namespace
python3 $TKE events cls-xxxxxxxx -n my-namespace                   # recent events
python3 $TKE scale cls-xxxxxxxx -n my-namespace --name my-deploy --replicas 4
python3 $TKE restart cls-xxxxxxxx -n my-namespace --name my-deploy

In-cluster commands shell out to kubectl against an SDK-fetched kubeconfig. kubectl must be installed in the sandbox (pip install doesn't ship it).

When to Use

• List TKE clusters across regions
• Check node health and node-pool resource usage
• List Deployments / StatefulSets / DaemonSets in a namespace
• List Services / Pods / recent Events
• Scale a workload up or down
• Rolling restart a Deployment (e.g. after a config change)
• Fetch kubeconfig for ad-hoc kubectl work

Dependencies

pip install tencentcloud-sdk-python
brew install kubectl   # macOS;  apt install kubectl on Debian/Ubuntu

Quick start — list clusters

import os
from tencentcloud.common import credential
from tencentcloud.tke.v20180525 import tke_client, models

cred = credential.EnvironmentVariableCredential().get_credential()
client = tke_client.TkeClient(cred, os.environ["TENCENTCLOUD_REGION"])

req = models.DescribeClustersRequest()
req.Limit = 100
resp = client.DescribeClusters(req)
for c in resp.Clusters:
    print(c.ClusterId, c.ClusterName, c.ClusterStatus, c.ClusterVersion)

Cluster IDs look like cls-xxxxxxxx. The ap-hongkong region typically holds the production clusters; DescribeClusters is region-scoped — call it per region you care about.

Workflows

Get cluster details + worker node count

req = models.DescribeClustersRequest()
req.ClusterIds = ["cls-xxxxxxxx"]
resp = client.DescribeClusters(req)
c = resp.Clusters[0]
print(c.ClusterName, c.ClusterStatus, c.ClusterNodeNum, c.ClusterVersion)

List worker nodes (and their CVM instance types)

req = models.DescribeClusterInstancesRequest()
req.ClusterId = "cls-xxxxxxxx"
req.Limit = 100
resp = client.DescribeClusterInstances(req)
for i in resp.InstanceSet:
    print(i.InstanceId, i.InstanceRole, i.InstanceState, i.NodePoolId)

Fetch kubeconfig

req = models.DescribeClusterKubeconfigRequest()
req.ClusterId = "cls-xxxxxxxx"
req.IsExtranet = True            # False for VPC-internal kubeconfig
resp = client.DescribeClusterKubeconfig(req)

# Save and use immediately
import os, pathlib
kubeconfig = pathlib.Path(os.path.expanduser("~/.kube/config-tke-cls-xxxxxxxx"))
kubeconfig.parent.mkdir(parents=True, exist_ok=True)
kubeconfig.write_text(resp.Kubeconfig)
print("export KUBECONFIG=" + str(kubeconfig))

Many TKE clusters expose only the internal API endpoint by default. If IsExtranet=True returns an empty / unusable config, the cluster's public API access isn't enabled — set IsExtranet=False and run kubectl from a host inside the same VPC (e.g. CVM, jump host).

Run kubectl commands (with the fetched kubeconfig)

import subprocess

KUBECONFIG = os.path.expanduser("~/.kube/config-tke-cls-xxxxxxxx")
NS = "acedatacloud"

def kubectl(*args):
    return subprocess.run(
        ["kubectl", f"--kubeconfig={KUBECONFIG}", *args],
        check=True, capture_output=True, text=True,
    ).stdout

print(kubectl("get", "pods", "-n", NS))
print(kubectl("get", "deploy", "-n", NS))
print(kubectl("get", "svc", "-n", NS))
print(kubectl("get", "events", "-n", NS, "--sort-by=.lastTimestamp"))

Describe a misbehaving pod

print(kubectl("describe", "pod", "<pod-name>", "-n", NS))
print(kubectl("logs", "<pod-name>", "-n", NS, "--tail=200"))

Scale a Deployment

# To 4 replicas. Confirm with the user before running for prod workloads.
print(kubectl("scale", "deploy/platform-backend", "-n", NS, "--replicas=4"))

Rolling restart a Deployment

# Forces every pod to recycle through the rolling-update strategy.
print(kubectl("rollout", "restart", "deploy/platform-backend", "-n", NS))
print(kubectl("rollout", "status", "deploy/platform-backend", "-n", NS, "--timeout=300s"))

List node pools (TKE concept above raw nodes)

req = models.DescribeClusterNodePoolsRequest()
req.ClusterId = "cls-xxxxxxxx"
resp = client.DescribeClusterNodePools(req)
for np in resp.NodePoolSet:
    print(np.NodePoolId, np.Name, np.LifeState, np.DesiredNodesNum, np.AutoscalingGroupId)

Troubleshooting flow

1. python: DescribeClusters → cluster status / version
2. python: DescribeClusterInstances → any nodes "failed" / "running"
3. kubectl get events → recent failures (image pulls, scheduling, OOM)
4. kubectl get pods → which pod is in CrashLoopBackOff / ImagePullBackOff
5. kubectl describe pod <name> → conditions, events on the pod
6. kubectl logs <name> --tail=200 → application logs
7. (optional) tencentcloud-cls skill → CLS query for the same window

Important reminders

• Confirm scale / restart actions with the user before running for production workloads. A replicas=0 typo takes the service down.
• Kubeconfigs contain a long-lived bearer token. Treat the file like a credential — chmod 600, never commit, regenerate after offboarding people.
• Internal vs external endpoint: IsExtranet=False gives a kubeconfig usable only from inside the cluster VPC. From a laptop, use IsExtranet=True and ensure the cluster has a public API endpoint enabled (TKE console → Cluster → Basic Info → API Server access).
• Region matters. Cluster cls-xxxxxxxx in ap-hongkong is invisible from a TKE client constructed for ap-guangzhou.

Console links

• TKE console: https://console.cloud.tencent.com/tke2/cluster
• API reference: https://www.tencentcloud.com/document/product/457/31862