GCP MCP Server

A Model Context Protocol (MCP) server for Google Cloud Platform (GCP) that enables AI assistants to interact with GCP services, particularly focused on log analysis and root cause investigation.

Features

• Cloud Logging Integration: Query and analyze GCP Cloud Logging data

• Real-time Log Streaming: Stream logs for immediate analysis

• Error Pattern Detection: Identify common error patterns and anomalies

• Multi-Project Support: Work across multiple GCP projects

• Secure Authentication: Uses GCP service account credentials

• Root Cause Analysis: Tools to help with quick RC findings

Related MCP server: CloudWatch Logs MCP Server

Supported GCP Services

• Cloud Logging: Query, filter, and analyze logs

• Cloud Monitoring: Retrieve metrics and alerts (planned)

• Error Reporting: Access error statistics and details (planned)

• Cloud Trace: Distributed tracing analysis (planned)

Installation

⚡ Quick Install

git clone https://github.com/JayRajGoyal/gcp-mcp.git
cd gcp-mcp
./install.sh

Claude Code Integration (One Command!)

The easiest way to add this MCP server to Claude Code:

# If you have gcloud configured (recommended):
claude mcp add gcp-logs -e GOOGLE_APPLICATION_CREDENTIALS=/Users/$USER/.config/gcloud/application_default_credentials.json -- python3.11 -m gcp_mcp.cli --project YOUR_PROJECT_ID

Or with a service account key file:

claude mcp add gcp -- python3.11 -m gcp_mcp.cli --credentials /path/to/your/service-account-key.json

Manual Configuration (Alternative)

Add this to your Claude Code configuration:

{
  "mcpServers": {
    "gcp": {
      "command": "python3.11",
      "args": ["-m", "gcp_mcp.cli", "--credentials", "/path/to/your/credentials.json"],
      "cwd": "/path/to/gcp-mcp"
    }
  }
}

Prerequisites

• Python 3.8 or higher

• GCP project with appropriate APIs enabled

• Service account with necessary permissions

Manual Setup

1. Clone the repository:

git clone https://github.com/JayRajGoyal/gcp-mcp.git
cd gcp-mcp

2. Install dependencies:

pip install -r requirements.txt

3. Run with your credentials:

python -m gcp_mcp.cli --credentials /path/to/your/credentials.json

Usage

Starting the Server

python -m gcp_mcp.server

Available Tools

Log Query

Query GCP Cloud Logging with advanced filters:

query_logs(project_id, filter, limit, time_range)

Log Analysis

Analyze logs for patterns and anomalies:

analyze_logs(project_id, service_name, time_range)

Error Investigation

Find and analyze error patterns:

investigate_errors(project_id, service_name, time_range)

Configuration

Create a config.json file:

{
  "default_project": "your-gcp-project-id",
  "log_retention_days": 30,
  "max_results": 1000,
  "excluded_log_names": [
    "projects/your-project/logs/cloudaudit.googleapis.com%2Fdata_access"
  ]
}

Authentication

The server supports multiple authentication methods:

1. Service Account Key File: Set GOOGLE_APPLICATION_CREDENTIALS

2. Application Default Credentials: For GCE, Cloud Shell, etc.

3. User Credentials: Via gcloud auth application-default login

Required GCP Permissions

Your service account needs the following IAM roles:

• roles/logging.viewer - Read access to Cloud Logging

• roles/monitoring.viewer - Read access to Cloud Monitoring (optional)

• roles/errorreporting.viewer - Read access to Error Reporting (optional)

Development

Running Tests

pytest tests/

Code Formatting

black gcp_mcp/
isort gcp_mcp/

Type Checking

mypy gcp_mcp/

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests

5. Run the test suite

6. Submit a pull request

License

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

Security

• Never commit service account keys to the repository

• Use environment variables for sensitive configuration

• Follow GCP security best practices

• Report security vulnerabilities via email

Support

• Create an issue for bug reports or feature requests

• Check existing issues before creating new ones

• Provide detailed information including logs and configuration

Roadmap

• Cloud Monitoring integration

• Error Reporting tools

• Cloud Trace analysis

• BigQuery log export support

• Alerting and notification tools

• Dashboard generation

• Cost analysis tools