Log Analyzer With CloudWatch Logs

A service based on the Model Context Protocol (MCP) that provides AI assistants with the ability to access and analyze AWS CloudWatch logs, supporting log search, summary generation, and cross-service correlation.

Monitoring Developer tools #Log analysis #AI integration #AWS logs #MCP protocol .Python

rating : 2.5 points

downloads : 29

update time : 2025-04-28

What is Log Analyzer with MCP?

Log Analyzer with MCP is a service based on the Model Context Protocol (MCP) standard that allows AI assistants (such as Claude) to directly interact with AWS CloudWatch logs. Through the standardized MCP interface, AI can browse log groups, search for specific log content, generate log summaries, and identify error patterns.

How to use Log Analyzer with MCP?

Before use, you need to configure AWS credentials and the Claude desktop client. After installation, the AI assistant can query log information through natural language, and the system will automatically convert it into a CloudWatch log query.

Applicable scenarios

Suitable for scenarios that require AI-assisted analysis of AWS cloud service logs, such as fault troubleshooting, performance analysis, and security auditing. Particularly suitable for correlated analysis of logs across multiple AWS services.

Main features

Log browsing and searchBrowse all CloudWatch log groups and search for specific log content by keywords or advanced query syntax

Log Insights querySupport the full CloudWatch Logs Insights query syntax to perform complex log analysis

Intelligent log summaryAI automatically generates log summaries and identifies error patterns and abnormal trends

Cross-service log correlationAutomatically correlate relevant logs from different AWS services to provide a complete request tracking view

Advantages and limitations

Advantages

Standardized interface: Provide a unified access method through the MCP protocol

AI optimization: Log query experience optimized for AI assistants such as Claude

No coding required: Query complex logs through natural language

Cross-service correlation: Automatically correlate relevant logs from multiple AWS services

Limitations

Only support AWS CloudWatch logs

Require configuration of AWS access permissions

Query performance is limited by the CloudWatch Logs service

How to use

Installation preparation

Ensure that the uv package manager and Python environment are installed

Set up the environment

Create a virtual environment and install dependencies

Configure AWS credentials

Configure AWS access permissions according to the documentation

Integrate the AI assistant

Update the configuration file of the Claude desktop client to connect to the MCP server

Usage examples

Error troubleshootingIdentify recent errors in the system and their context

Performance analysisAnalyze API response time trends

Frequently Asked Questions

What AWS permissions are required?

Which AI assistants are supported?

Is there a time limit for queries?

Related resources

Introduction to the MCP protocol

Official introduction document of the Model Context Protocol

AWS CloudWatch documentation

Official AWS CloudWatch Logs documentation

GitHub repository

Project source code and latest updates

🚀 Log Analyzer Based on MCP

A Model Context Protocol (MCP) server that enables AI assistants to access and analyze, search, and correlate logs from AWS CloudWatch Logs.

🏗️ Architecture

Architecture Diagram

🔌 Model Context Protocol (MCP)

As described by Anthropic:

MCP is an open protocol that standardizes the way applications provide context to large language models (LLMs). Think of MCP as the USB-C port for AI applications. Just as USB-C provides a universal way for your devices to connect to various peripherals and accessories, MCP provides a universal way for AI models to connect to different data sources and tools.

This repository contains an example client and server that allow an AI assistant like Claude to interact with CloudWatch logs in an AWS account. For more information, read the Introduction.

✨ Features

Detect and search CloudWatch log groups
Search logs using CloudWatch Logs Insights query syntax
Generate log summaries and identify error patterns
Correlate logs across multiple AWS services
Provide optimized tools suitable for AI assistants like Claude

Full feature list

📦 Installation

Prerequisites

uv Python package and project manager
An AWS account with the CloudWatch Logs service enabled
Configured AWS credentials

Setup

# Clone the repository
git clone https://github.com/awslabs/Log-Analyzer-with-MCP.git
cd Log-Analyzer-with-MCP

# Create a virtual environment and install dependencies
uv sync
source .venv/bin/activate  # Use `.venv\Scripts\activate` on Windows

🚀 Quick Start

Ensure that you have configured your AWS credentials as described here.
Update the claude_desktop_config.json file to match the correct configuration described in the AI Integration Guide.
Open the Claude desktop app and start chatting!

For more examples and advanced usage, refer to the Detailed Usage Guide.