gcp-mcp-server

LokiMCPUniverse/gcp-mcp-server

3.2

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The GCP MCP Server is a comprehensive Model Context Protocol server designed to integrate Google Cloud Platform APIs with GenAI applications.

Tools

  1. gcp_compute_list_instances

    Lists VM instances in a specified project and zone.

  2. gcp_storage_create_bucket

    Creates a new Cloud Storage bucket with specified configurations.

  3. gcp_bigquery_query

    Executes a SQL query on BigQuery datasets.

  4. gcp_functions_deploy

    Deploys a Cloud Function with specified parameters.

  5. gcp_vertexai_deploy_model

    Deploys a machine learning model using Vertex AI.

GCP MCP Server

Gcp Mcp Server

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A comprehensive Model Context Protocol (MCP) server for integrating Google Cloud Platform (GCP) APIs with GenAI applications.

Features

  • Comprehensive GCP Service Coverage:

    • Compute Engine: VM instances, instance groups, autoscaling
    • Cloud Storage: Buckets, objects, signed URLs, lifecycle policies
    • Cloud Functions: Deploy, invoke, manage serverless functions
    • BigQuery: Datasets, tables, queries, data warehouse operations
    • Cloud SQL: Database instances, backups, replicas
    • Kubernetes Engine (GKE): Cluster management, workload deployment
    • Cloud Run: Serverless container deployment
    • Pub/Sub: Topics, subscriptions, message publishing
    • Cloud IAM: Service accounts, roles, policies
    • Cloud Build: CI/CD pipelines, build triggers
    • Vertex AI: ML model deployment and management
    • Cloud Logging & Monitoring: Metrics, alerts, log analysis

  • Authentication Methods:

    • Service Account JSON keys
    • Application Default Credentials (ADC)
    • OAuth 2.0 for user authentication
    • Workload Identity Federation
    • Impersonation support

  • Enterprise Features:

    • Multi-project support
    • Cross-region operations
    • VPC-SC (Service Controls) support
    • Cost management and billing alerts
    • Compliance and security scanning
    • Resource labeling and organization

Installation

pip install gcp-mcp-server

Or install from source:

git clone https://github.com/asklokesh/gcp-mcp-server.git
cd gcp-mcp-server
pip install -e .

Configuration

Create a .env file or set environment variables:

# GCP Credentials
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-key.json
GCP_PROJECT_ID=your-project-id
GCP_REGION=us-central1
GCP_ZONE=us-central1-a

# OR use Application Default Credentials
GOOGLE_CLOUD_PROJECT=your-project-id

# Optional Settings
GCP_QUOTA_PROJECT_ID=quota-project-id
GCP_IMPERSONATE_SERVICE_ACCOUNT=service-account@project.iam.gserviceaccount.com
GCP_TIMEOUT=30
GCP_MAX_RETRIES=3

# Multi-Project Support
GCP_PROD_PROJECT_ID=prod-project-id
GCP_PROD_CREDENTIALS=/path/to/prod-key.json

GCP_DEV_PROJECT_ID=dev-project-id
GCP_DEV_CREDENTIALS=/path/to/dev-key.json

Quick Start

Basic Usage

from gcp_mcp import GCPMCPServer

# Initialize the server
server = GCPMCPServer()

# Start the server
server.start()

Claude Desktop Configuration

Add to your Claude Desktop config:

{
  "mcpServers": {
    "gcp": {
      "command": "python",
      "args": ["-m", "gcp_mcp.server"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/service-account-key.json",
        "GCP_PROJECT_ID": "your-project-id",
        "GCP_REGION": "us-central1"
      }
    }
  }
}

Available Tools

Compute Engine Operations

List Instances
{
  "tool": "gcp_compute_list_instances",
  "arguments": {
    "project": "my-project",
    "zone": "us-central1-a",
    "filter": "status=RUNNING"
  }
}
Create Instance
{
  "tool": "gcp_compute_create_instance",
  "arguments": {
    "project": "my-project",
    "zone": "us-central1-a",
    "name": "my-instance",
    "machine_type": "e2-medium",
    "source_image": "projects/debian-cloud/global/images/family/debian-11",
    "disk_size_gb": 20,
    "network_tags": ["web-server"],
    "metadata": {
      "startup-script": "#!/bin/bash\napt-get update\napt-get install -y nginx"
    }
  }
}

Cloud Storage Operations

Create Bucket
{
  "tool": "gcp_storage_create_bucket",
  "arguments": {
    "project": "my-project",
    "bucket_name": "my-unique-bucket",
    "location": "US",
    "storage_class": "STANDARD",
    "lifecycle_rules": [
      {
        "action": {"type": "Delete"},
        "condition": {"age": 365}
      }
    ]
  }
}
Upload Object
{
  "tool": "gcp_storage_upload_object",
  "arguments": {
    "bucket": "my-bucket",
    "object_name": "data/file.txt",
    "content": "File content here",
    "content_type": "text/plain",
    "metadata": {"key": "value"}
  }
}

BigQuery Operations

Create Dataset
{
  "tool": "gcp_bigquery_create_dataset",
  "arguments": {
    "project": "my-project",
    "dataset_id": "my_dataset",
    "location": "US",
    "description": "My dataset description"
  }
}
Execute Query
{
  "tool": "gcp_bigquery_query",
  "arguments": {
    "project": "my-project",
    "query": "SELECT * FROM `project.dataset.table` WHERE date = CURRENT_DATE()",
    "use_legacy_sql": false,
    "maximum_bytes_billed": 1000000000
  }
}

Cloud Functions Operations

Deploy Function
{
  "tool": "gcp_functions_deploy",
  "arguments": {
    "project": "my-project",
    "region": "us-central1",
    "name": "my-function",
    "runtime": "python39",
    "entry_point": "main",
    "source_code": "def main(request):\n    return 'Hello World!'",
    "trigger_type": "http",
    "memory_mb": 256
  }
}

GKE Operations

Create Cluster
{
  "tool": "gcp_gke_create_cluster",
  "arguments": {
    "project": "my-project",
    "zone": "us-central1-a",
    "cluster_name": "my-cluster",
    "initial_node_count": 3,
    "machine_type": "e2-standard-4",
    "enable_autopilot": false,
    "enable_autoscaling": true,
    "min_nodes": 1,
    "max_nodes": 10
  }
}

Cloud Run Operations

Deploy Service
{
  "tool": "gcp_cloudrun_deploy",
  "arguments": {
    "project": "my-project",
    "region": "us-central1",
    "service_name": "my-service",
    "image": "gcr.io/my-project/my-image:latest",
    "memory": "512Mi",
    "cpu": "1",
    "max_instances": 100,
    "allow_unauthenticated": true
  }
}

Vertex AI Operations

Deploy Model
{
  "tool": "gcp_vertexai_deploy_model",
  "arguments": {
    "project": "my-project",
    "region": "us-central1",
    "model_name": "my-model",
    "endpoint_name": "my-endpoint",
    "machine_type": "n1-standard-4",
    "min_replica_count": 1,
    "max_replica_count": 3
  }
}

Advanced Configuration

Multi-Project Support

from gcp_mcp import GCPMCPServer, ProjectConfig

# Configure multiple projects
projects = {
    "production": ProjectConfig(
        project_id="prod-project-id",
        credentials_path="/path/to/prod-key.json",
        default_region="us-central1"
    ),
    "development": ProjectConfig(
        project_id="dev-project-id",
        credentials_path="/path/to/dev-key.json",
        default_region="us-east1"
    ),
    "data-warehouse": ProjectConfig(
        project_id="dw-project-id",
        credentials_path="/path/to/dw-key.json",
        default_region="us-central1"
    )
}

server = GCPMCPServer(projects=projects, default_project="production")

Service Account Impersonation

from gcp_mcp import GCPMCPServer, ImpersonationConfig

impersonation_config = ImpersonationConfig(
    target_service_account="elevated-sa@project.iam.gserviceaccount.com",
    lifetime_seconds=3600,
    delegates=[],
    target_scopes=["https://www.googleapis.com/auth/cloud-platform"]
)

server = GCPMCPServer(impersonation_config=impersonation_config)

Cost Management

from gcp_mcp import GCPMCPServer, CostConfig

cost_config = CostConfig(
    enable_cost_tracking=True,
    budget_alert_threshold=1000.0,  # USD
    cost_allocation_labels=["team", "environment", "project"],
    bigquery_billing_export_dataset="billing_export"
)

server = GCPMCPServer(cost_config=cost_config)

Integration Examples

See the examples/ directory for complete integration examples:

  • basic_operations.py - Common GCP operations
  • multi_project.py - Managing multiple GCP projects
  • data_pipeline.py - Building data pipelines with BigQuery and Dataflow
  • ml_deployment.py - Deploying ML models with Vertex AI
  • infrastructure_as_code.py - Managing infrastructure programmatically
  • cost_optimization.py - Cost analysis and optimization

Security Best Practices

  1. Use service accounts with minimal permissions
  2. Enable audit logging for all API calls
  3. Implement VPC Service Controls for data exfiltration prevention
  4. Use Workload Identity for GKE workloads
  5. Rotate service account keys regularly
  6. Enable organization policies for security constraints
  7. Use Cloud KMS for encryption key management

Error Handling

The server provides detailed error information:

try:
    result = server.execute_tool("gcp_compute_create_instance", {
        "name": "my-instance",
        "zone": "invalid-zone"
    })
except GCPError as e:
    print(f"GCP error: {e.error_code} - {e.message}")
    if e.error_code == "ZONE_NOT_FOUND":
        print(f"Valid zones: {e.valid_zones}")

Performance Optimization

  1. Use batch operations where available
  2. Enable request caching for read operations
  3. Implement regional failover for high availability
  4. Use Cloud CDN for static content
  5. Optimize BigQuery queries with partitioning and clustering

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests.

License

MIT License - see LICENSE file for details