mcpField
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gcp-cloud-run

gcp-cloud-run

github.com/alphaonedevCloud & DevOps51 installs
Repository

About gcp-cloud-run

gcp-cloud-run is a skill that guides Google Cloud provisioning and operations through the official Python client libraries. It provides architecture advice, cost estimates, and pre-flight guidance, while the SDKs perform the operations.

Key features

  1. 01Guide GCP service selection
  2. 02Estimate and optimize costs
  3. 03Review IAM and Workload Identity Federation needs
  4. 04Advise on infrastructure-as-code approaches
  5. 05Guide Vertex AI and agent architecture

Use cases

  1. 01Compare GCP service options
  2. 02Plan cloud costs before provisioning
  3. 03Check identity and access prerequisites
  4. 04Design Vertex AI multi-agent workflows

MCP support

The skill notes that Vertex AI Extensions are MCP-compatible. It does not describe an MCP server or specific MCP tools it exposes.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/alphaonedev/openclaw-graph --skill gcp-cloud-run

After installing, type/gcp-cloud-runin Claude Code to confirm it loaded.

Details

From the project README

gcp-cloud-run

Google Cloud Integration

This skill delegates all GCP provisioning and operations to the official Google Cloud Python client libraries.

# Core GCP client library
pip install google-cloud-python

# Vertex AI + Agent Engine (AI/ML workloads)
pip install google-cloud-aiplatform

# Specific service clients (install only what you need)
pip install google-cloud-bigquery      # BigQuery
pip install google-cloud-storage       # Cloud Storage
pip install google-cloud-pubsub        # Pub/Sub
pip install google-cloud-run           # Cloud Run

SDK Docs: https://github.com/googleapis/google-cloud-python Vertex AI SDK: https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk

Use the Google Cloud Python SDK for all GCP provisioning and operational actions. This skill provides architecture guidance, cost modeling, and pre-flight requirements — the SDK handles execution.

Architecture Guidance

Consult this skill for:

  • GCP service selection and trade-off analysis
  • Cost estimation and optimization (committed use discounts, sustained use)
  • Pre-flight IAM / Workload Identity Federation requirements
  • IaC approach (Terraform AzureRM vs Deployment Manager vs Config Connector)
  • Integration patterns with Google Workspace and other GCP services
  • Vertex AI Agent Engine for multi-agent workflow design

Agent & AI Capabilities

CapabilityTool
LLM agentsVertex AI Agent Engine
Model servingVertex AI Model Garden
RAGVertex AI Search + Embeddings API
Multi-agentAgent Development Kit (google/adk-python)
MCPVertex AI Extensions (MCP-compatible)

Reference

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