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.
README
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]
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