Google Cloud
Google Cloud agent skills and MCPs
105 skills
cloud-run-basicsgoogle16KManages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).bigquery-basicsgoogle15KManages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.google-cloud-recipe-onboardinggoogle14KGuides a developer's first steps on Google Cloud, covering account creation, billing setup, project management, and deploying a first resource. Use when a new developer wants to initialize their first Google Cloud project, configure billing, and verify deployment. Don't use for enterprise organization setup (use Google Cloud Setup guided flow for that instead). Don't use for complex multi-project architectures.google-cloud-recipe-authgoogle14KProvides expert guidance on authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.cloud-sql-basicsgoogle13KThis file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for relatiogoogle-cloud-waf-securitygoogle13KGenerates security-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate workloads, identify security requirements, and provide actionable recommendations for IAM, network security, data protection, and operational security.google-cloud-waf-cost-optimizationgoogle13KGenerates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify cost requirements and constraints, and provide actionable recommendations for build, deploy, and manage the workload cost-efficiently in Google Cloud.google-cloud-waf-reliabilitygoogle12KGenerates guidance for reliability, resilience, availability, redundancy, fault-tolerance, and disaster recovery (DR) for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework. Use when the user asks to evaluate, design, or improve the reliability, resilience, availability, or disaster recovery capabilities of Google Cloud workloads.gke-basicsgoogle12KManages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, configuring Workload Identity, or deciding between Autopilot and Standard modes. Don't use for specialized GKE networking (use gke-networking), advanced security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).alloydb-basicsgoogle12KManages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.google-cloud-networking-observabilitygoogle10KInvestigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.gcloudgoogle9KProvides safety-critical validation, guardrails, and data reduction for gcloud CLI operations across Google Cloud Platform (GCP) services and infrastructure. Use when planning, generating, constructing, proposing, describing, or executing any gcloud CLI commands - including when answering questions about gcloud syntax, or formatting flags. Don't use when writing Google Cloud client library code or raw REST/gRPC API requests.google-cloud-waf-operational-excellencegoogle8.1KGenerates operations-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify operational requirements, and provide actionable recommendations for deployment, monitoring, and incident management.google-cloud-waf-performance-optimizationgoogle8KGenerates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.google-cloud-waf-sustainabilitygoogle7.9KProvides recommendations for environmental sustainability, carbon footprint reduction, and energy efficiency based on the Sustainability pillar of the Google Cloud Well-Architected Framework (WAF). Use when the user asks to assess, design, or optimize Google Cloud workloads for sustainability—including the shared responsibility model, selecting low-carbon regions (CFE%), reducing resource and AI/ML energy waste, designing efficient software and storage lifecycles, or measuring and tracking emissworkload-manager-basicsgoogle5.7KUse this skill to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server isbigquery-ai-mlgoogle5.6KLeverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect chabigtable-basicsgoogle5.4KAssists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.gke-upgradesgoogle5.4KPlans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters. Produces upgrade plans, pre/post-upgrade checklists, maintenance runbooks with gcloud commands, release channel strategy, and troubleshooting guides. Handles node pool upgrade strategies (surge, blue-green), version compatibility, PDB management, and workload-specific concerns (stateful, GPU, operators). Use this skill whenever the user mentions GKE gke-observabilitygoogle5.3KConfigures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection, and to troubleshoot Managed Service for Prometheus (GMP) issues such as missing metrics, unhealthy scrape targets, PodMonitoring misconfiguration, rule/alert evaluation failures, and monitoring permission errors. Don't use to configure local application logging frameworks or external APMs outsidgke-networkinggoogle5.2KPlans, configures, and manages core GKE cluster networking. Covers private clusters, VPC-native configurations, DNS, node egress, Dataplane V2, and IP planning. Use when designing GKE networking layouts, configuring private clusters, setting up Dataplane V2, planning GKE IP ranges, or managing VPC- native cluster modes. Don't use for application ingress, load balancing, or service networking (use gke-service-networking instead).gke-golden-pathgoogle5.2KProvides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up workload autoscaling specifically (use gke-workload-scaling instead).gke-compute-classesgoogle5.2KConfigures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.gke-reliabilitygoogle5.2KImproves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).gke-app-onboardinggoogle5.2KManages GKE application onboarding, covering containerization, deployment manifests, and migration. Use when onboarding or deploying an application to GKE for the first time, or containerizing an app for GKE. Don't use for general GKE cluster administration or upgrades (use gke-basics or gke-upgrades instead).gke-cluster-creationgoogle5.2KPlans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Use when creating GKE clusters, provisioning GKE environments, selecting cluster modes, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).gke-storagegoogle5.2KManages GKE storage, including PVCs, PersistentVolumes, Filestore, and GCS FUSE. Use when configuring GKE storage, creating PVCs, or setting up GCS FUSE on GKE. For diagnosing storage failures (volume attach/mount errors, disk performance/node storage pressure, or Cloud Storage FUSE OOM), use the gke-storage-troubleshooting skill instead. Don't use for database administration or replication strategies outside volume provisioning context.gke-backup-drgoogle5.2KConfigures Backup for GKE: the BackupRestore cluster addon, BackupPlan and RestorePlan resources, restore workflows, and CMEK-encrypted backups. Use for backup policies, disaster recovery, or GKE cluster restores. Don't use for database backups.gke-multitenancygoogle5.2KPlans and configures multi-tenancy on GKE. Covers namespace isolation, RBAC planning for teams, resource quotas, LimitRanges, network isolation, and cost allocation. Use when designing GKE multi-tenancy, configuring GKE namespaces, setting up resource quotas, or isolating GKE teams. Don't use for single-tenant cluster configuration or general deployment instructions (use gke-basics or gke-app-onboarding instead).gke-inferencegoogle5.1KDeploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).gke-batch-hpcgoogle5.1KRuns batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).detection-engineering-coverage-evaluationgoogle5.1KAutomates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, generating new YARA-L 2.0 rules to close coverage gaps, and with user approval, deploy them to SecOps. Don't use when asked to perform threat hunting actions, and SOC investigative actions.bigquery-bigframesgoogle5.1KGenerates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.google-cloud-recipe-foundation-buildergoogle5KDeploys a baseline landing zone foundation for a Google Cloud Organization, establishing security guardrails using Organization Policies, resource hierarchy folders and projects, billing association, and centralized logging and monitoring. Deploys Google Cloud's recommended security controls and architecture. Use when setting up a new Google Cloud Organization or establishing a secure, enterprise-grade landing zone foundation. Don't use for individual project onboarding (use google-cloud-recipe-datalineage-bigquery-asset-impact-analysisgoogle4.9KAnalyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querygoogle-cloud-solution-architecturegoogle4.6KInteractively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Use when designing end-to-end cloud solutions, selecting and integrating Google Cloud services, generating architecture diagrams, or conducting requirements discovery for new cloud workloads or migrations. Don't use for single-product tasks (use product-specific skills), initial onboarding or authentication (use cloud-logging-query-generationgoogle4.4KGenerates Logging Query Language (LQL) queries for Google Cloud Logging from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.google-cloud-solution-agentic-ai-bidirectional-streaminggoogle4.1KGuides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-gke-cluster-autoscalergoogle4.1KTrigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).cloud-monitoring-metric-selectiongoogle4KRetrieve, query, and identify relevant Google Cloud Monitoring metric descriptors for a GCP service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.google-cloud-storage-basicsgoogle4KStores, retrieves, and manages data as objects in Cloud Storage (Google Cloud Storage, or GCS) buckets. Use when you need to interact with Cloud Storage — set up a Storage MCP server (remote or local Toolbox), create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM, ACLs, public access prevention), set storage classes (Standard, Nearline, Coldline, Archive), manage lifecycle and cost, protect data (versionigoogle-cloud-global-frontend-configurationgoogle4KGuides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backengke-workload-scalinggoogle3.8KManages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.google-cloud-solution-agentic-ai-data-science-workflowgoogle3.8KDesigns a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.google-cloud-solution-agentic-ai-borderless-data-lakehousegoogle3.8KDiscovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-dgoogle-cloud-solution-build-deploy-agentsgoogle3.8KDesigns, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.gke-cost-optimizationgoogle3.8KOptimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).gke-cost-analysisgoogle3.8KAnswer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizigoogle-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle3.7KDiscovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use fodatalineage-summarygoogle3.7KSummarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).google-cloud-solution-rag-enterprise-search-gke-sqldbgoogle3.7KDiscovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database gke-platform-securitygoogle3.7KPlans, configures, and hardens platform-level Google Kubernetes Engine (GKE) cluster security. Covers cluster add-ons (Secret Manager enablement), RBAC hardening (disabling insecure bindings, audit tools), Binary Authorization, enabling Shielded Nodes, GKE Sandbox cluster enablement, GKE IAM roles, and cross-service authentication IAM patterns. Use when securing cluster control planes, hardening GKE RBAC, enabling Shielded Nodes, enabling GKE Sandbox runtime, enabling cluster-wide security add-ogoogle-cloud-solution-n-tier-serverless-web-appgoogle3.7KAssists in designing and implementing secure n-tier serverless web applications and microservices on Google Cloud. Use when users need architecture designs, security checklists, Terraform code, or deployment guidance for multi-tier serverless apps, regional data residency / European sovereignty compliance, zero-trust private VPC networking, or Private Service Connect. Don't use for VM, GKE, or non-Google Cloud architectures.gke-workload-securitygoogle3.7KAudits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (`audit_cluster.sh`), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (`gVisor`), enforcing Pod Security Standards (`restricted` labeling), and mounting Secret Manager secrets vicloud-logging-configuration-basicsgoogle3.6KConfigure single-project Google Cloud Logging: regional log buckets, log sinks, log views, restricting or hiding sensitive logs in the default view (_Default) filter, IAM permissions for views (Logs View Accessor, IAM conditions), logs-based metrics, log exclusions, and sampling. Don't use for cross-project logging or multi-project setups.gke-service-networkinggoogle3.5KConfigures GKE edge networking, traffic routing, load balancing, and private service endpoints. Use when configuring Gateway API manifests, standard Ingress, Cloud Armor WAF security policies, Container-Native Load Balancing (NEGs), Private Service Connect (PSC), or Google-managed SSL certificates on GKE. Don't use for core cluster IP planning, Dataplane V2 network policies, or node NAT egress (use gke-networking instead).gke-productionizegoogle3.5KOrchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling, gke-platform-security, gke-workload-security, gke-service-networking, ggoogle-cloud-solution-guided-gke-ai-migrationgoogle3.4KGuides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to self-hosted inference on GKE, or asks follow-up questions during such a migration (hardware sizing, model staging, manifest generation, validation, traffic cutover). DO NOT use forcloud-logging-cross-project-configurationgoogle3.4KConfigure and troubleshoot Google Cloud cross-project centralized logging and read-time aggregation. Use when: - Setting up log routing from multiple projects/folders/organizations to a central log bucket. - Creating cross-project log sinks and configuring central log buckets. - Troubleshooting cross-project routing. Don't use for single-project basic configurations.gke-ai-troubleshooting-handle-disruption-gpu-tpugoogle3.4KDiagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU/TPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, networcloud-monitoring-chart-generationgoogle3.4KGenerates Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSerigke-manifest-generationgoogle3.3KGenerates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters. Use when creating or modifying GKE deployment manifests, configuring container security contexts, setting CPU/memory resource limits, defining readiness/liveness/startup probes, mounting secrets and volumes, configuring GKE Gateway API routes, targeting Spot VMs, or deploying AI model inference workloads (vLLM, TGI, Gemma). Don't use for live cluster operations, pod trogke-workload-troubleshootinggoogle3.3KDiagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.spanner-basicsgoogle3.2KAssists in provisioning instances and databases, designing performant schemas, and querying data in Spanner. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.gke-ai-troubleshooting-jobset-interruptiongoogle3.2KDiagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.gke-ai-troubleshooting-tpu-vbar-oomgoogle3.1KDiagnoses and prevents vbar_control_agent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbar_control_agent crashes, memory cgroup OOMs in serial console logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container OOM troubleshoogoogle-cloud-slo-alert-configurationgoogle3KConfigures PromQL-based Service Level Objective (SLO) alerting policies for Google Cloud resources registered in App Hub or individually specified. Generates Terraform output. Use when the user asks to configure an SLO or Service Level Objective. Don't use for standard alerting policies.managed-airflow-migrationsgoogle3KProvides guidance for migrating Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers migration to Airflow 2.11.1 (MSAA Gen 2 and 3) and Airflow 3 (MSAA Gen 3), including environment inspection, GCS download/upload and scanning patterns for breaking changes. Use when migrating the DAG code to newer Airflow version. Don't use when checking DAG run failures unrelated to code migration.google-cloud-filestore-autoscalegoogle2.8KInspects Google Cloud Filestore capacity and utilization, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds (custom thresholds apply globally across projects in session memory), or preventing out-of-space outages. Don't use for Cloud Storage (GCS) buckets, Persistent Disk block storage, or Netmanaged-airflow-dag-authoringgoogle2.8KProvides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.cloud-monitoring-list-time-series-requestgoogle2.7KGenerates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.iam-helper-for-privileged-access-managementgoogle2.6KManages the end-to-end lifecycle of on-demand, temporary access using Privileged Access Manager (PAM). Use when a user asks to create, read, update, or delete PAM entitlements, request temporary access, or approve/deny pending PAM grants. Do NOT use for permanent IAM policy bindings, troubleshooting IAM permission errors, or general Google Cloud resource provisioning.application-design-center-design-deploygoogle2.5KProcesses GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.google-cloud-storage-fusegoogle2.5KMounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), oriam-helper-for-policy-simulatorgoogle2.5KSafely simulates and applies Google Cloud IAM v1 (Allow) policy changes. Uses the Policy Simulator to replay historical access logs against proposed policies to prevent breaking active workloads before applying the changes. Use when simulating or applying IAM v1 allow policies across Projects, Folders, or Organizations. Don't use for analyzing IAM v2 deny policies, VPC Service Controls, or performing general policy troubleshooting.cloud-build-basicsgoogle2.4KTeaches the fundamentals of Google Cloud Build (GCB). Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.gke-ai-troubleshooting-tpu-dynamic-slices-monitoringgoogle2.4KMonitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).gke-ai-troubleshooting-tpu-metrics-monitoringgoogle2.4KMonitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL. Use when monitoring TensorCore duty cycle, TPU memory, node readiness, multi-host TPU node pool availability, host maintenance or preemption interruptions, and calculating MTTR or MTBI metrics for GKE TPUs. Don't use for general non-TPU GKE workload monitoring or non-metric TPU debugging.cloud-monitoring-promql-querygoogle2.3KGenerates valid PromQL queries from Cloud Monitoring metric descriptors and resource parameters. Use when asked to create, generate, write, or format PromQL queries, PromQL strings, or PromQL aggregations for Cloud Monitoring metrics and resources. Don't use for raw metric discovery or metric selection.cloud-databases-onboardinggoogle2.3KGuides users through discovering their database requirements, recommends a Google Cloud database based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', or when a user wants to create a new database on Google Cloud. Don't use for general Google Cloud maintenance, managing existing databases, or database migrations.google-cloud-scc-querygoogle2.3KQueries and retrieves active security findings, external exposures, toxic combinations, vulnerabilities, threats, and sensitive data risks from Google Cloud Security Command Center. Use when retrieving details for a security finding by its name, validating finding scope (e.g., verifying findingClass is TOXIC_COMBINATION, VULNERABILITY, EXTERNAL_EXPOSURE, or THREAT), or fetching finding details for triage. Don't use to draft remediations, apply patches, or execute configurations.gke-alert-configurationgoogle2.2KConfigures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standmanaged-airflow-dag-troubleshootinggoogle2.2KProvides guidance for troubleshooting Apache Airflow DAGs (failed DAG runs and task instances) in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Use when figuring out reasons for DAG run or task instance failures. Don't use when looking for overall recommendations for Managed Airflow environment performance.gke-custom-golden-image-discoverygoogle2.2KDiscovers golden base images for creating GKE custom node images based on technical specifications or context clues. Use when finding the golden base image for custom GKE node creation, mapping cluster configuration parameters (GKE version, OS, architecture, accelerators, gVisor, cgroups) to image names, or querying GKE base image maps. Don't use for general GKE cluster creation (use gke-cluster-creation) or standard node pool management (use gke-basics).google-cloud-solution-multi-agent-securitygoogle2.2KDesigns, deploys, and secures Google Cloud Agent Gateway solutions. Use when the user needs to configure multi-agent security, ingress (CLIENT_TO_AGENT), or egress (AGENT_TO_ANYWHERE) patterns involving Model Armor, IAP, and Agent Registry. Don't use for general Cloud Load Balancing or basic VPC setup not related to Agent Gateways.google-cloud-solution-hybrid-search-alloydbgoogle2KDiscovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T ugoogle-cloud-storage-bucket-architectgoogle1.4KCreates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, ogke-node-notreadygoogle1.4KDiagnoses GKE nodes reporting NotReady or Unknown status by inspecting node conditions, events, kubelet/containerd logs, and node metrics, then proposing safe remediations. Use when nodes show NotReady, when the kubelet stops posting node status, or when workloads are evicted or stuck Pending due to node health. Don't use for pod-level application failures (use gke-workload-troubleshooting), autoscaler scale-up/scale-down decisions (use gke-cluster-autoscaler), or non-GKE compute.bigquery-observabilitygoogleProvides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscabigquery-slot-cost-optimizergoogleAnalyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).cloud-run-alert-configurationgoogleConfigures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKEdbt-sf-to-bq-translatorgoogleTranslates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.gke-storage-troubleshootinggoogleDiagnoses GKE persistent-storage failures — volume attach/mount errors (Regional PD on optimized VMs, fsGroup mount timeouts), disk-performance and node storage-pressure issues, slow-disk Pod-creation failures, volume-expansion problems, Local SSD / Hyperdisk Storage Pool creation errors, and Cloud Storage FUSE OOM. Use when Pods are stuck in ContainerCreating, volumes fail to attach or mount, or nodes report storage pressure. Don't use for routine storage provisioning or StorageClass/PVC authorgke-workload-identitygoogleDiagnoses Workload Identity Federation for GKE authentication failures for Pods (403 "iam.serviceAccounts.getAccessToken" / permission denied, "could not find default credentials", or GKE metadata server unreachable) by verifying cluster and node-pool Workload Identity configuration, the Kubernetes ServiceAccount (KSA) to IAM binding (direct principal binding and legacy Google ServiceAccount impersonation), target-resource IAM roles, and gke-metadata-server health. Use when a Pod cannot authentigoogle-cloud-filestore-auditinggoogleAudits Google Cloud Filestore instances across projects for disaster recovery readiness (missing or stale backups), security access governance (overly permissive NFS export rules, 0.0.0.0/0 exposure, missing ROOT_SQUASH), and reliability compliance (Physical Zone Isolation PZI and Physical Zone Separation PZS). Use when assessing storage health posture, auditing NFS export permissions, identifying unprotected file shares, or validating zone failure domains. Don't use for Cloud Storage buckets, Pgoogle-cloud-filestore-nfs-browsergoogleInspects, searches, and reads files and POSIX metadata on Google Cloud Filestore (NFS) instances without local NFS client packages or root privileges. Use when browsing Filestore shares, searching files, reading remote logs, or inspecting file attributes; don't use for Cloud Storage buckets, Cloud NetApp Volumes, Persistent Disks, or modifying/deleting files.iam-helper-for-policy-managementgoogleStreamlines the creation, modification, and management of IAM allow policies (v1) and deny policies (v2). Manages access control across Resource Manager resources (Organization, Folder, Project) and individual resources. Use when creating, updating, listing, or deleting IAM allow policies or deny policies. Don't use for access denial troubleshooting (use iam-helper-for-troubleshooting), temporary privileged access (use iam-helper-for-privileged-access-management), configuring VPC Service Controliam-helper-for-troubleshootinggoogleDiagnoses, remediates, and manages Google Cloud Identity and Access Management (IAM) access issues. Supports two distinct operational modes: (1) Requester Flow for developers encountering access denials (capturing error context, self-service PAM JIT activations, elevated developer self-remediation, or logging structured tickets), and (2) Resolver Flow for privileged administrators (authoritative Policy Troubleshooter analysis, deny policy exemptions, least-privilege role discovery, and PAM/IAM psecops-casesgoogleManage Google Security Operations (SecOps) SOAR cases throughout their lifecycle. Use when listing, creating, inspecting, updating, or closing SOAR cases; adding investigative comments and notes; updating case priority or description; or linking and grouping security alerts within cases. Supports both remote Google SecOps MCP tools and local fallback tools. Don't use for SIEM UDM searches or detection rule authoring.secops-detection-engineeringgoogleAuthor, validate, test, and deploy YARA-L 2.0 detection rules and evaluate end-to-end detection coverage gaps in Google SecOps. Use when writing new detection rules, tuning existing rules, validating syntax, testing logic against historical telemetry, or evaluating detection coverage against threat intelligence blogs, CVE disclosures, and Threat Detection Opportunities (TDOs) using synthetic UDM events and long-running coverage analysis. Don't use for alert triage (use secops-triage), deep forensecops-huntgoogleExpert guidance for proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authorinsecops-investigategoogleExpert guidance for deep security incident and entity investigations in Google SecOps. Use when investigating cases, analyzing entities (hosts, IPs, domains, hashes, users), extracting and searching UDM events, performing asset and user timeline analysis, and detecting lateral movement across enterprise networks. Don't use for detection rule authoring or YARA-L tuning (use secops-detection-engineering), proactive hypothesis-driven hunting (use secops-hunt), initial alert triage (use secops-triagsecops-triagegoogleExpert guidance for security alert triage in Google SecOps. Use when investigating and triaging security alerts, determining false positives vs. true positives, assessing entity risk, adjusting alert severity or priority, and closing or escalating alerts and cases. Don't use for deep multi-hop incident investigations across host timelines (use secops-investigate), proactive threat hunting or retroactive IoC sweeps (use secops-hunt), or authoring new detection rules (use secops-detection-engineergcp-cloud-runsickn331.6KSpecialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub.gcp-cloud-runaj-geddesDeploy containerized applications on Google Cloud Run with automatic scaling, traffic management, and service mesh integration. Use for container-based serverless computing.
26 servers
MCP Toolbox for Databasesgoogleapis17KOpen source MCP server specializing in easy, fast, and secure tools for Databases.Google Cloudgoogleapis920Interact with Google Cloud services and manage your cloud resources.Google Cloud RunGoogleCloudPlatform630Official MCP Server to deploy to Google Cloud Run.Gemini Cloud AssistGoogleCloudPlatform68An MCP Server that connects to Gemini Cloud Assist APIs, enabling you to manage and troubleshoot your Google Cloud environment using natural language.Google Cloudkrzko80An MCP server for interacting with your Google Cloud resources and services.Google Cloud Loggingswen12811An MCP server for accessing Google Cloud Logging data and services.GCPenesbol6Access and manage Google Cloud Platform (GCP) services and resources.GCPMCP-MirrorManage and interact with Google Cloud Platform (GCP) resources through an AI assistant.GCP Toolsgcp-toolsAutomate Google Cloud Platform infrastructure setup and GitHub repository configuration for cloud-native projects.Terragrunt GCP MCP Toolspolspol1Manage Google Cloud Platform (GCP) infrastructure using Terragrunt, with support for experimental features like AutoDevOps and cost management.Google Cloud Healthcare API (FHIR)Kartha-AIProvides healthcare tools for interacting with FHIR resources on Google Cloud Healthcare API and public medical research APIs like PubMed.Google Cloud BigQueryGoogle Cloud BigQueryGoogle Cloud BigQuery provides advanced analytical insight for data agents. Its connector gives assistants BigQuery context for exploring datasets, asking analytical questions, and working with large-scale cloud data from natural language.BigQueryergut148Server implementation for Google BigQuery integration that enables direct BigQuery database access and querying capabilitiesBigQueryLucasHild131BigQuery database integration with schema inspection and query capabilitiesBigQuerytakuya02065Access Google BigQuery to understand dataset structures and execute SQL queries.BigQuery Analysisgotalab2Execute and validate SQL queries against Google BigQuery. It safely runs SELECT queries under 1TB and returns results in JSON format.MCP BigQueryMoustenSecurely access BigQuery datasets with intelligent caching, schema tracking, and query analytics via Supabase integration.BigQuerycodearrangerInspect database schemas and execute queries on Google BigQuery.BigQuery-Read-Only-MCP-ServerhugonissarA secure, self-hosted Model Context Protocol (MCP) server for Google BigQuery. Hard table allowlists, per-query scan ceilings, built-in rate limiting, and predictable costs on Cloud Run. Works with Claude, ChatGPT, Cursor, Gemini, and any MCP-compatible AI agent.BigQuerysweatcoExplore, query, and manage data in Google BigQuery.BigQuerytakada-atAccess and cache Google Cloud BigQuery metadata.Snow Leopard BigQuerySnowLeopard-AI10Interact with Google BigQuery databases using natural language queries and schema exploration.GCP Bucket Connector (data-connectors-ai)nagarjunrRead-only MCP server for GCP Cloud Storage: list objects in a bucket.CData AlloyDBCDataSoftwareA read-only MCP server for AlloyDB, enabling LLMs to query live data directly from AlloyDB databases.Google BigQuery by CDataCDataSoftwareConnect to Google BigQuery databases using CData's MCP Server. Requires a separate CData JDBC Driver license.CData Google SpannerCDataSoftwareA read-only MCP server for Google Spanner, enabling LLMs to query live data.