Google Cloud Docs MCP Server

An MCP (Model Context Protocol) server that provides AI assistants with access to Google Cloud Platform documentation. This enables Claude and other MCP-compatible assistants to search, fetch, and understand GCP documentation in real-time.

Table of Contents

• Features
• How It Works
• Prerequisites
• Installation
• Configuration
• Usage
• Available Tools
• Supported Topics
• Project Structure
• Development
• Contributing
• License

Features

• Free-form Search: Search GCP documentation with natural language queries
• Content Extraction: Extract clean markdown content from documentation pages
• 80+ Topic Mappings: Pre-configured mappings for common GCP topics
• Relevance Scoring: Smart ranking of search results by relevance
• API Reference: Access REST API documentation for GCP services
• 20+ GCP Products: Support for major Google Cloud services

How It Works

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude    │────▶│  MCP Server     │────▶│  Google Cloud    │
│  (or other  │     │  (this project) │     │  Documentation   │
│  MCP client)│◀────│                 │◀────│  (cloud.google   │
└─────────────┘     └─────────────────┘     │   .com/docs)     │
                                           └──────────────────┘

1. Query Processing: When Claude receives a GCP-related question, it calls the MCP server tools
2. Search Strategy: The server uses a multi-step search approach:
   • Google Search targeting site:cloud.google.com
   • Google Cloud's internal search API (fallback)
   • 80+ pre-configured topic mappings (fallback)
3. Content Extraction: Uses Cheerio to parse HTML and extract clean markdown
4. Relevance Scoring: Results are scored and sorted by query relevance
5. Response: Returns structured JSON with documentation content

Technical Details

• Protocol: MCP (Model Context Protocol) over stdio transport
• Content Parsing: HTML to Markdown conversion with Cheerio
• Search: Combines Google Search scraping with fallback topic mappings
• Output Format: Structured JSON with markdown content

Prerequisites

• Node.js >= 18.0.0
• npm or yarn
• Claude Desktop, Claude Code, or other MCP-compatible client

Installation

From Source

# Clone the repository
git clone https://github.com/longngo192/gcpdoc-mcp
cd gcpdoc-mcp

# Install dependencies
npm install

# Build the project
npm run build

Quick Start

npm install && npm run build

Configuration

Claude Desktop

Add to your Claude Desktop config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "google-cloud-docs": {
      "command": "node",
      "args": [
        "/path/to/google-cloud-docs-mcp/dist/index.js"
      ]
    }
  }
}

Claude Code CLI

# Add the MCP server
claude mcp add google-cloud-docs -- node /path/to/dist/index.js

# Verify installation
claude mcp list

Usage

Once configured, Claude will automatically use the GCP documentation tools when you ask questions about Google Cloud services.

Example Queries

"How do I set up VPC peering between two GCP projects?"
"What are the steps to enable CMEK encryption for Cloud Storage?"
"How to configure Cloud SQL high availability?"
"Show me GKE autoscaling configuration options"
"How to set environment variables in Cloud Run?"

Direct Tool Usage

If using MCP protocol directly:

// Search documentation
{
  "method": "tools/call",
  "params": {
    "name": "search_google_cloud_docs",
    "arguments": {
      "query": "vpc peering between projects"
    }
  }
}

// Fetch specific documentation
{
  "method": "tools/call",
  "params": {
    "name": "fetch_google_cloud_doc",
    "arguments": {
      "path": "vpc/docs/vpc-peering"
    }
  }
}

Available Tools

1. search_google_cloud_docs

Search GCP documentation with free-form queries. Primary tool for GCP questions.

Parameter	Type	Required	Description
query	string	Yes	Natural language search query
product	string	No	Filter by GCP product (e.g., 'compute', 'storage')

Example:

{
  "query": "how to share encrypted bucket cross account",
  "product": "storage"
}

2. fetch_google_cloud_doc

Fetch content from a specific documentation page.

Parameter	Type	Required	Description
path	string	Yes	Documentation path after cloud.google.com/

Example:

{
  "path": "storage/docs/encryption/customer-managed-keys"
}

3. list_google_cloud_products

List all supported GCP products and their documentation paths.

No parameters required.

4. get_api_reference

Get REST API reference for a GCP service.

Parameter	Type	Required	Description
service	string	Yes	Service name (e.g., 'compute', 'storage')
resource	string	No	Specific API resource (e.g., 'instances', 'buckets')

Example:

{
  "service": "compute",
  "resource": "instances"
}

Supported Topics

The server includes 80+ topic mappings for accurate search results:

Category	Topics
Storage & Encryption	encrypt, bucket, cmek, kms, customer managed, object storage
IAM & Security	iam, role, service account, impersonation, workload identity
Networking	vpc, peering, shared vpc, firewall, load balancer, dns, nat
Database	cloud sql, high availability, mysql, postgres, replica, failover
BigQuery	partition, cluster, materialized view, schedule
GKE	gke, autoscaling, node pool, horizontal pod autoscaler, helm
Serverless	cloud run, environment variable, cloud function, deploy
Container	docker, artifact registry, cloud build
Pub/Sub	pubsub, topic, subscription
Data Processing	dataflow, dataproc, composer, airflow, spark
Monitoring	logging, monitoring, metric, alert, dashboard, trace
Infrastructure	terraform, deployment manager, gcloud

Supported GCP Products

ID	Name	Description
compute	Compute Engine	Virtual machines and infrastructure
storage	Cloud Storage	Object storage service
bigquery	BigQuery	Data warehouse and analytics
kubernetes	GKE	Managed Kubernetes service
functions	Cloud Functions	Serverless compute platform
run	Cloud Run	Serverless containers
pubsub	Pub/Sub	Messaging and event ingestion
sql	Cloud SQL	Managed relational databases
firestore	Firestore	NoSQL document database
spanner	Cloud Spanner	Globally distributed database
ai	Vertex AI	Machine learning platform
iam	IAM	Identity and Access Management
vpc	VPC	Virtual Private Cloud networking
loadbalancing	Cloud Load Balancing	Global load balancing
logging	Cloud Logging	Log management and analysis
monitoring	Cloud Monitoring	Infrastructure monitoring

Project Structure

google-cloud-docs-mcp/
├── src/
│   └── index.ts          # Main MCP server implementation
├── dist/                  # Compiled JavaScript (generated)
├── package.json          # Project configuration
├── tsconfig.json         # TypeScript configuration
├── .gitignore           # Git ignore rules
├── LICENSE              # MIT License
└── README.md            # This file

Development

Running in Development Mode

# With hot reload
npm run dev

# Build and run
npm run build && npm start

Testing the Server

# Test MCP initialization
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}' | node dist/index.js

# Test tools/list
echo '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}' | node dist/index.js

Building

npm run build

Contributing

Contributions are welcome! Here's how you can help:

Getting Started

1. Fork the repository
2. Clone your fork: git clone https://github.com/yourusername/google-cloud-docs-mcp.git
3. Create a feature branch: git checkout -b feature/your-feature-name
4. Make your changes
5. Run tests and build: npm run build
6. Commit your changes: git commit -m "Add your feature"
7. Push to your fork: git push origin feature/your-feature-name
8. Open a Pull Request

Contribution Ideas

• Add more topic mappings: Expand the topicMappings object in src/index.ts
• Support more GCP products: Add entries to GOOGLE_CLOUD_PRODUCTS
• Improve content extraction: Enhance the Cheerio parsing logic
• Add caching: Implement response caching to reduce API calls
• Add tests: Write unit tests for search and content extraction
• Documentation: Improve README or add usage examples

Code Style

• Use TypeScript
• Follow existing code patterns
• Add comments for complex logic
• Test your changes before submitting

Reporting Issues

Found a bug or have a suggestion? Please open an issue with:

• Clear description of the problem
• Steps to reproduce
• Expected vs actual behavior
• Your environment (Node.js version, OS)

Acknowledgments

• Model Context Protocol - The protocol that enables AI-tool communication
• Cheerio - Fast HTML parsing
• Google Cloud Documentation - The source of all documentation

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with love for the GCP and AI community