"kubernetes" matching MCP tools:

• browse_by_tag

  AI Agents Overflow

  Browse the knowledge base by technology tag at the START of a task. Call this when beginning work with a specific technology to discover what verified knowledge already exists — before you hit problems. Examples of useful tags: 'pytorch', 'cuda', 'fastapi', 'docker', 'ros2', 'numpy', 'jetson', 'arm64', 'postgresql', 'redis', 'kubernetes', 'react'. Returns a list of questions (title + tags + score) for the given tag, ordered by community score. Call `get_answers` on relevant results.

  Connector

• convoopen

  InsideOut (Riley)

  WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• deploy_staging

  RationalBloks

  Deploy a project to the staging environment. This triggers: (1) Schema validation, (2) Docker image build, (3) GitHub commit, (4) Kubernetes deployment, (5) Database migrations. The operation is ASYNCHRONOUS - it returns immediately with a job_id. Use get_job_status with the job_id to monitor progress. Deployment typically takes 2-5 minutes depending on schema complexity. If deployment fails, check: (1) Schema format is FLAT (no 'fields' nesting), (2) Every field has a 'type' property, (3) Foreign keys reference existing tables, (4) No PostgreSQL reserved words in table/field names. Use get_project_info to see if the deployment succeeded.

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• convoopen

  InsideOut (Riley)

  WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• deploy_graph_staging

  RationalBloks

  Deploy a graph project to the staging environment. This triggers: (1) Schema validation, (2) Neo4j entity code generation, (3) Docker image build, (4) GitHub commit, (5) Kubernetes deployment with Neo4j instance. The operation is ASYNCHRONOUS — returns immediately with a job_id. Use get_job_status to monitor progress. Deployment typically takes 2-5 minutes. Use get_graph_project_info to verify deployment succeeded.

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• list_k8s_api_resources

  mcp

  Retrieves the available API groups and resources from a Kubernetes cluster. This is similar to running `kubectl api-resources`.

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• apply_k8s_manifest

  Google GKE

  Applies a Kubernetes manifest to a cluster using server-side apply. This is similar to running `kubectl apply --server-side`.

  Connector

• get_k8s_version

  Google GKE

  Retrieves Kubernetes client and server versions for a given cluster. This is similar to running `kubectl version`.

  Connector

• get_k8s_resource

  mcp

  Gets one or more Kubernetes resources from a cluster. Resources can be filtered by type, name, namespace, and label selectors. Returns the resources in YAML format. This is similar to running `kubectl get`.

  Connector

• list_k8s_api_resources

  Google GKE

  Retrieves the available API groups and resources from a Kubernetes cluster. This is similar to running `kubectl api-resources`.

  Connector

• check_k8s_auth

  mcp

  Checks whether an action is allowed on a Kubernetes resource. This is similar to running `kubectl auth can-i`.

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• get_graph_project_info

  RationalBloks

  Get detailed graph project information including Kubernetes deployment status, Neo4j database health, pod status, and resource usage. Use this after deployment to verify the graph project is running correctly.

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• create_cluster

  Google GKE

  Creates a new GKE cluster in a given project and location. It's recommended to read the [GKE documentation](https://docs.cloud.google.com/kubernetes-engine/docs/concepts/configuration-overview) to understand cluster configuration options. Cluster creation will default to Autopilot mode, as recommended by GKE best practices. If the user explicitly wants to create a Standard cluster, you need to set autopilot.enabled=false in the cluster configuration. This is similar to running `gcloud container clusters create-auto` or `gcloud container clusters create`.

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• create_cluster

  mcp

  Creates a new GKE cluster in a given project and location. It's recommended to read the [GKE documentation](https://docs.cloud.google.com/kubernetes-engine/docs/concepts/configuration-overview) to understand cluster configuration options. Cluster creation will default to Autopilot mode, as recommended by GKE best practices. If the user explicitly wants to create a Standard cluster, you need to set autopilot.enabled=false in the cluster configuration. This is similar to running `gcloud container clusters create-auto` or `gcloud container clusters create`.

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• get_k8s_version

  mcp

  Retrieves Kubernetes client and server versions for a given cluster. This is similar to running `kubectl version`.

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• get_skills

  MidOS - MCP Community Library

  Get reusable skills (step-by-step procedures) for specific technologies. Skills are validated, executable guides covering common tasks like deployment, testing, migration, and configuration. Filter by technology stack to find relevant skills. Args: stack: Filter by technology (e.g. 'python', 'fastapi', 'react', 'kubernetes') Returns: JSON array of skills with name, description, and stack compatibility

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• get_k8s_resource

  Google GKE

  Gets one or more Kubernetes resources from a cluster. Resources can be filtered by type, name, namespace, and label selectors. Returns the resources in YAML format. This is similar to running `kubectl get`.

  Connector

• check_k8s_auth

  Google GKE

  Checks whether an action is allowed on a Kubernetes resource. This is similar to running `kubectl auth can-i`.

  Connector

• delete_k8s_resource

  Google GKE

  Deletes a Kubernetes resource from a cluster. This is similar to running `kubectl delete`.

  Connector

• delete_k8s_resource

  mcp

  Deletes a Kubernetes resource from a cluster. This is similar to running `kubectl delete`.

  Connector