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GCP MCP Log Diagnostics

by kanhaiworld
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Python
Remote

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.

Related MCP server: GCP MCP Server

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

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