GCP MCP Log Diagnostics

This project provides tools for diagnosing Google Cloud Platform (GCP) logs using the Model Context Protocol (MCP) and Google Gemini AI.

Overview

• diagnose.py: A script that uses Gemini AI to analyze GCP logs fetched via MCP tools. It diagnoses issues, identifies root causes, and suggests fixes.

• log_mcp_server.py: An MCP server that exposes tools for fetching logs from GCP Cloud Logging.

Prerequisites

• Python 3.8+

• Google Cloud Project with appropriate permissions for Cloud Logging

• Gemini API key

Setup

1. Clone or download the project files.

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables in a .env file:

   GEMINI_API_KEY=your_gemini_api_key_here

   Ensure your Google Cloud credentials are configured (e.g., via gcloud auth application-default login).

4. Run the diagnosis:

   python diagnose.py

Usage

The diagnose.py script is configured to fetch the last 2 hours of ERROR and CRITICAL logs from Cloud Run and provide a diagnosis. You can modify the query in the script or extend it for other resource types.

Dependencies

• python-dotenv: For loading environment variables

• google-generativeai: For interacting with Gemini AI

• fastmcp: For MCP client and server functionality

• google-cloud-logging: For accessing GCP Cloud Logging

License

[Add license information if applicable]