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GCP MCP Log Diagnostics

Enables diagnosis of Google Cloud Platform logs using Gemini AI via MCP tools, fetching logs from Cloud Logging for issue analysis and root cause identification.

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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

Clone or download the project files.
Install dependencies:
```
pip install -r requirements.txt
```
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).
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]