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
.envfile:GEMINI_API_KEY=your_gemini_api_key_hereEnsure 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 variablesgoogle-generativeai: For interacting with Gemini AIfastmcp: For MCP client and server functionalitygoogle-cloud-logging: For accessing GCP Cloud Logging
License
[Add license information if applicable]
