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krzko

🤖 A Model Context Protocol (MCP) server for Google Cloud (GCP)

Publisherkrzko
Repositorygoogle-cloud-mcp
LanguageTypeScript
Forks
20
Stars
78
Available tools
0
Transport typestdio
Categories
LicenseApache-2.0
Links
  • Connect tools to AI workflows

    Google Cloud exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    78 stars and 20 forks from the linked repository.

Google Cloud MCP Server

A Model Context Protocol server that connects to Google Cloud services to provide context and tools for interacting with your Google Cloud resources.

Services

Supported Google Cloud services:

Billing

Manage and analyse Google Cloud billing with cost optimisation insights:

Tools: gcp-billing-list-accounts, gcp-billing-get-account-details, gcp-billing-list-projects, gcp-billing-get-project-info, gcp-billing-list-services, gcp-billing-list-skus, gcp-billing-analyse-costs, gcp-billing-detect-anomalies, gcp-billing-cost-recommendations, gcp-billing-service-breakdown

Example prompts:

  • "Show me all my billing accounts"
  • "Analyse costs for project my-app-prod-123 for the last 30 days"
  • "Generate cost recommendations for billing account billingAccounts/123456-789ABC-DEF012"
  • "Check for billing anomalies in project my-ecommerce-456"

Error Reporting

Monitor and analyse application errors with automated investigation and remediation suggestions:

Tools: gcp-error-reporting-list-groups, gcp-error-reporting-get-group-details, gcp-error-reporting-analyse-trends

Example prompts:

  • "Show me error groups from project my-webapp-prod-789 for the last hour"
  • "Get details for error group projects/my-app-123/groups/xyz789"
  • "Analyse error trends for service my-api in project analytics-prod-456"

IAM

Query and analyse IAM policies and permissions:

Tools: gcp-iam-get-project-policy, gcp-iam-test-project-permissions, gcp-iam-test-resource-permissions, gcp-iam-validate-deployment-permissions, gcp-iam-list-deployment-services, gcp-iam-analyse-permission-gaps

Example prompts:

  • "Get IAM policy for project my-webapp-prod-123"
  • "Test if I have storage.buckets.create permission on project data-lake-456"
  • "Check deployment permissions for Cloud Run in project microservices-789"
  • "Analyse permission gaps for deploying to GKE cluster in project k8s-prod-321"

Logging

Query and filter log entries from Google Cloud Logging:

Tools: gcp-logging-query-logs, gcp-logging-query-time-range, gcp-logging-search-comprehensive

Example prompts:

  • "Show me logs from project my-app-prod-123 from the last hour with severity ERROR"
  • "Search for logs containing 'timeout' from service my-api in project backend-456"
  • "Query logs for resource type gce_instance in project compute-prod-789"

Spanner

Interact with Google Cloud Spanner databases:

Tools: gcp-spanner-execute-query, gcp-spanner-list-tables, gcp-spanner-list-instances, gcp-spanner-list-databases, gcp-spanner-query-natural-language, gcp-spanner-query-count

Example prompts:

  • "List all databases in Spanner instance my-instance in project ecommerce-prod-123"
  • "Execute SQL: SELECT COUNT(*) FROM users in database user-db in project my-app-456"
  • "Show me table structure for orders in database inventory-db in project retail-789"

Monitoring

Retrieve and analyse metrics from Google Cloud Monitoring:

Tools: gcp-monitoring-query-metrics, gcp-monitoring-list-metric-types, gcp-monitoring-query-natural-language

Example prompts:

  • "Show me CPU utilisation metrics for project web-app-prod-123 for the last 6 hours"
  • "List available metric types for Compute Engine in project infrastructure-456"
  • "Query memory usage for instances in project backend-services-789"

Profiler

Analyse application performance with Google Cloud Profiler:

Tools: gcp-profiler-list-profiles, gcp-profiler-analyse-performance, gcp-profiler-compare-trends

Example prompts:

  • "List CPU profiles from project my-java-app-123 for the last 24 hours"
  • "Analyse performance bottlenecks in service my-api in project backend-prod-456"
  • "Compare heap profiles for deployment v1.2 vs v1.3 in project performance-test-789"

Trace

Analyse distributed traces from Google Cloud Trace:

Tools: gcp-trace-get-trace, gcp-trace-list-traces, gcp-trace-find-from-logs, gcp-trace-query-natural-language

Example prompts:

  • "Get trace details for ID abc123def456 in project distributed-app-789"
  • "Show me failed traces from project microservices-prod-123 from the last hour"
  • "Find logs related to trace xyz789 in project web-backend-456"
  • "Query traces for service checkout-api in project ecommerce-prod-321"

Quick Start

Once configured, you can interact with Google Cloud services using natural language:

"What are my current billing costs for project my-webapp-prod-123?"
"Show me errors from project ecommerce-api-456 in the last hour"
"Check if I have permission to deploy to Cloud Run in project microservices-789"
"Find logs containing 'database timeout' from project backend-prod-321 yesterday"
"List Spanner databases in instance prod-db for project data-store-654"
"What's the CPU usage of Compute Engine instances in project infrastructure-987?"

Authentication

This server supports two methods of authentication with Google Cloud:

  1. Service Account Key File (Recommended): Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of your service account key file. This is the standard Google Cloud authentication method.

  2. Environment Variables: Set GOOGLE_CLIENT_EMAIL and GOOGLE_PRIVATE_KEY environment variables directly. This is useful for environments where storing a key file is not practical.

The server will also use the GOOGLE_CLOUD_PROJECT environment variable if set, otherwise it will attempt to determine the project ID from the authentication credentials.

Installation

bash
# Clone the repository
git clone https://github.com/krzko/google-cloud-mcp.git
cd google-cloud-mcp

# Install dependencies
pnpm install

# Build
pnpm build

Authenticate to Google Cloud:

bash
gcloud auth application-default login

Configure the mcpServers in your client:

json
{
  "mcpServers": {
      "google-cloud-mcp": {
          "command": "node",
          "args": [
              "/Users/foo/code/google-cloud-mcp/dist/index.js"
          ],
          "env": {
              "GOOGLE_APPLICATION_CREDENTIALS": "/Users/foo/.config/gcloud/application_default_credentials.json"
          }
      }
  }
}

Development

Starting the server

bash
# Build the project
pnpm build

# Start the server
pnpm start

Development mode

bash
# Build the project
pnpm build

# Start the server and inspector
npx -y @modelcontextprotocol/inspector node dist/index.js

Troubleshooting

Server Timeout Issues

If you encounter timeout issues when running the server with Smithery, try the following:

  1. Enable debug logging by setting debug: true in your configuration
  2. Ensure lazyAuth: true is set to defer authentication until it's actually needed
  3. Ensure your credentials file is accessible and valid
  4. Check the logs for any error messages

Important: Authentication is still required for operation, but with lazy loading enabled, the server will start immediately and authenticate when needed rather than during initialization.

Authentication Issues

The server supports two methods of authentication:

  1. Service Account Key File: Set GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of your service account key file
  2. Environment Variables: Set GOOGLE_CLIENT_EMAIL and GOOGLE_PRIVATE_KEY environment variables

If you're having authentication issues, make sure:

  • Your service account has the necessary permissions
  • The key file is properly formatted and accessible
  • Environment variables are correctly set

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "google-cloud-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@krzko/google-cloud-mcp"
      ],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "your/service/account/key/or/leave/empty/if/injected",
        "GOOGLE_CLIENT_EMAIL": "your-service-account-email@your-project.iam.gserviceaccount.com",
        "GOOGLE_PRIVATE_KEY": "-----BEGIN PRIVATE KEY-----....-----END PRIVATE KEY-----",
        "GOOGLE_CLOUD_PROJECT": "your-gcp-project-id"
      }
    }
  }
}

Use Google Cloud MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Google Cloud is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Google Cloud as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Google Cloud MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "google-cloud": {
      "command": "npx",
      "args": [
        "-y",
        "google-cloud-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Google Cloud MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Google Cloud MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Google Cloud to help me with this task?
Google Cloud
Sure. I read it.
Here is what I found using Google Cloud.

Frequently asked questions

What is the Google Cloud MCP server used for?

Google Cloud is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Google Cloud MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Google Cloud with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Google Cloud MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Google Cloud connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Google Cloud MCP to TypingMind?

Google Cloud runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Google Cloud MCP provide in TypingMind?

Google Cloud exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Google Cloud MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Google Cloud requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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