cloud-infrastructureanalytics-data

Google Cloud Platform

eniayomi

by eniayomi

Integrate with Google Drive and GCloud Storage via Google Cloud Platform for seamless access to Compute Engine, BigQuery

Integrates with Google Cloud Platform services, providing tools for interacting with Compute Engine, Cloud Storage, Cloud Functions, Cloud Run, BigQuery, and more using official client libraries and robust error handling.

github stars

196

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Browser-based OAuth — no manual credential setupCovers major GCP services in one interfaceNatural language cloud management

best for

  • / Cloud engineers managing GCP infrastructure
  • / DevOps teams automating cloud operations
  • / Developers who prefer natural language over CLI commands
  • / Quick GCP resource monitoring and alerts

capabilities

  • / Authenticate with GCP using browser OAuth flow
  • / List and manage compute instances across projects
  • / Create and configure storage buckets and disks
  • / Set up VPC networks and firewall rules
  • / Monitor GKE clusters and databases
  • / View billing information and usage metrics

what it does

Manages Google Cloud Platform resources through natural language commands with automated OAuth authentication. Lets you control compute instances, storage, networking, databases, and monitoring without leaving your chat interface.

about

Google Cloud Platform is a community-built MCP server published by eniayomi that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate with Google Drive and GCloud Storage via Google Cloud Platform for seamless access to Compute Engine, BigQuery It is categorized under cloud infrastructure, analytics data. This server exposes 9 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Google Cloud Platform in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Google Cloud Platform is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

GCP MCP

A Model Context Protocol (MCP) server that enables AI assistants like Claude to interact with your Google Cloud Platform environment. This allows for natural language querying and management of your GCP resources during conversations.

GCP MCP Demo

Features

  • 🔍 Query and modify GCP resources using natural language
  • ☁️ Support for multiple GCP projects
  • 🌐 Multi-region support
  • 🔐 Secure credential handling (no credentials are exposed to external services)
  • 🏃‍♂️ Local execution with your GCP credentials
  • 🔄 Automatic retries for improved reliability

Prerequisites

  • Node.js
  • Claude Desktop/Cursor/Windsurf
  • GCP credentials configured locally (application default credentials)

Installation

  1. Clone the repository:
git clone https://github.com/eniayomi/gcp-mcp
cd gcp-mcp
  1. Install dependencies:
npm install

Configuration

Claude Desktop

  1. Open Claude desktop app and go to Settings -> Developer -> Edit Config

  2. Add the following entry to your claude_desktop_config.json:

via npm:

{
  "mcpServers": {
    "gcp": {
      "command": "sh",
      "args": ["-c", "npx -y gcp-mcp"]
    }
  }
}

If you installed from source:

{
  "mcpServers": {
    "gcp": {
      "command": "npm",
      "args": [
        "--silent",
        "--prefix",
        "/path/to/gcp-mcp",
        "start"
      ]
    }
  }
}

Replace /path/to/gcp-mcp with the actual path to your project directory if using source installation.

Cursor

  1. Open Cursor and go to Settings (⌘,)
  2. Navigate to AI -> Model Context Protocol
  3. Add a new MCP configuration:
{
  "gcp": {
    "command": "npx -y gcp-mcp"
  }
}

Windsurf

  1. Open ~/.windsurf/config.json (create if it doesn't exist)
  2. Add the MCP configuration:
{
  "mcpServers": {
    "gcp": {
      "command": "npx -y gcp-mcp"
    }
  }
}

GCP Setup

  1. Set up GCP credentials:

    • Set up application default credentials using gcloud auth application-default login
  2. Refresh your AI assistant (Claude Desktop/Cursor/Windsurf)

Usage

Start by selecting a project or asking questions like:

  • "List all GCP projects I have access to"
  • "Show me all Cloud SQL instances in project X"
  • "What's my current billing status?"
  • "Show me the logs from my Cloud Run services"
  • "List all GKE clusters in us-central1"
  • "Show me all Cloud Storage buckets in project X"
  • "What Cloud Functions are deployed in us-central1?"
  • "List all Cloud Run services"
  • "Show me BigQuery datasets and tables"

Available Tools

  1. run-gcp-code: Execute GCP API calls using TypeScript code
  2. list-projects: List all accessible GCP projects
  3. select-project: Select a GCP project for subsequent operations
  4. get-billing-info: Get billing information for the current project
  5. get-cost-forecast: Get cost forecast for the current project
  6. get-billing-budget: Get billing budgets for the current project
  7. list-gke-clusters: List all GKE clusters in the current project
  8. list-sql-instances: List all Cloud SQL instances in the current project
  9. get-logs: Get Cloud Logging entries for the current project

Example Interactions

  1. List available projects:
List all GCP projects I have access to
  1. Select a project:
Use project my-project-id
  1. Check billing status:
What's my current billing status?
  1. View logs:
Show me the last 10 log entries from my project

Supported Services

  • Google Compute Engine
  • Cloud Storage
  • Cloud Functions
  • Cloud Run
  • BigQuery
  • Cloud SQL
  • Google Kubernetes Engine (GKE)
  • Cloud Logging
  • Cloud Billing
  • Resource Manager
  • More coming soon...

Troubleshooting

To see logs:

tail -n 50 -f ~/Library/Logs/Claude/mcp-server-gcp.log

Common issues:

  1. Authentication errors: Ensure you've run gcloud auth application-default login
  2. Permission errors: Check IAM roles for your account
  3. API errors: Verify that required APIs are enabled in your project

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT

FAQ

What is the Google Cloud Platform MCP server?
Google Cloud Platform is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Google Cloud Platform?
This profile displays 34 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.734 reviews
  • Arjun Huang· Dec 12, 2024

    Strong directory entry: Google Cloud Platform surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Yash Thakker· Nov 11, 2024

    Google Cloud Platform has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Liam Khanna· Nov 3, 2024

    Google Cloud Platform is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Olivia Tandon· Oct 22, 2024

    We evaluated Google Cloud Platform against two servers with overlapping tools; this profile had the clearer scope statement.

  • Dhruvi Jain· Oct 2, 2024

    According to our notes, Google Cloud Platform benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Piyush G· Sep 17, 2024

    I recommend Google Cloud Platform for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Mia Ramirez· Sep 13, 2024

    We wired Google Cloud Platform into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Ira Rahman· Sep 5, 2024

    Google Cloud Platform is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Min Desai· Aug 24, 2024

    We wired Google Cloud Platform into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Shikha Mishra· Aug 8, 2024

    Strong directory entry: Google Cloud Platform surfaces stars and publisher context so we could sanity-check maintenance before adopting.

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