by eniayomi
Integrate with Google Drive and GCloud Storage via Google Cloud Platform for seamless access to Compute Engine, BigQuery
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.
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.
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.
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.
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
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Share your MCP server with the developer community
Strong directory entry: Google Cloud Platform surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Google Cloud Platform has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Google Cloud Platform is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Google Cloud Platform against two servers with overlapping tools; this profile had the clearer scope statement.
According to our notes, Google Cloud Platform benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
I recommend Google Cloud Platform for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We wired Google Cloud Platform into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Google Cloud Platform is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired Google Cloud Platform into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: Google Cloud Platform surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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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.
git clone https://github.com/eniayomi/gcp-mcp
cd gcp-mcp
npm install
Open Claude desktop app and go to Settings -> Developer -> Edit Config
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.
{
"gcp": {
"command": "npx -y gcp-mcp"
}
}
~/.windsurf/config.json (create if it doesn't exist){
"mcpServers": {
"gcp": {
"command": "npx -y gcp-mcp"
}
}
}
Set up GCP credentials:
gcloud auth application-default loginRefresh your AI assistant (Claude Desktop/Cursor/Windsurf)
Start by selecting a project or asking questions like:
run-gcp-code: Execute GCP API calls using TypeScript codelist-projects: List all accessible GCP projectsselect-project: Select a GCP project for subsequent operationsget-billing-info: Get billing information for the current projectget-cost-forecast: Get cost forecast for the current projectget-billing-budget: Get billing budgets for the current projectlist-gke-clusters: List all GKE clusters in the current projectlist-sql-instances: List all Cloud SQL instances in the current projectget-logs: Get Cloud Logging entries for the current projectList all GCP projects I have access to
Use project my-project-id
What's my current billing status?
Show me the last 10 log entries from my project
To see logs:
tail -n 50 -f ~/Library/Logs/Claude/mcp-server-gcp.log
Common issues:
gcloud auth application-default loginContributions are welcome! Please feel free to submit a Pull Request.
MIT
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.