Log-Analyzer-with MCP Server

Use This MCP server To

Search and analyze AWS CloudWatch Logs via AI assistants Correlate log events across multiple CloudWatch Log Groups Enable AI-driven troubleshooting using real-time log data Automate log data extraction for incident investigation Integrate CloudWatch Logs into AI-enhanced monitoring workflows Provide structured log context to LLMs for operational insights

README

Log Analyzer with MCP

A Model Context Protocol (MCP) server that provides AI assistants access to AWS CloudWatch Logs for analysis, searching, and correlation.

🏗️ Architecture

Architecture Diagram

🔌 Model Context Protocol (MCP)

As outlined by Anthropic:

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

This repository is an example client and server that allows an AI assistant like Claude to interact with CloudWatch logs in an AWS account. To learn more about MCP, read through the introduction.

✨ Features

Browse 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
AI-optimized tools for assistants like Claude

Detailed feature list

🚀 Installation

Prerequisites

The uv Python package and project manager
An AWS account with CloudWatch Logs
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  # On Windows, use `.venv\Scripts\activate`

🚦 Quick Start

Make sure to have configured your AWS credentials as described here
Update your claude_desktop_config.json file with the proper configuration outlined in the AI integration guide
Open Claude for Desktop and start chatting!

For more examples and advanced usage, see the detailed usage guide.

🤖 AI Integration

This project can be easily integrated with AI assistants like Claude for Desktop. See the AI integration guide for details.

📚 Documentation

Detailed Features
Usage Guide
AWS Configuration
Architecture Details
AI Integration
Troubleshooting

🔒 Security

See CONTRIBUTING for more information.

📄 License

This project is licensed under the Apache-2.0 License.

Log-Analyzer-with-MCP FAQ

How does Log-Analyzer-with-MCP connect to AWS CloudWatch?

It uses AWS SDK credentials configured in the environment to securely access CloudWatch Logs.

Can this MCP server handle large volumes of log data?

Yes, it supports efficient querying and pagination to manage extensive CloudWatch log datasets.

Is the server compatible with multiple AI models?

Yes, it follows the MCP standard, making it compatible with models like OpenAI GPT, Anthropic Claude, and Google Gemini.

How is data security maintained when accessing logs?

Access is controlled via AWS IAM roles and policies, ensuring secure and scoped permissions.

Can I customize the log search queries?

Yes, the server supports flexible search parameters to tailor log retrieval to specific needs.

Does it support real-time log updates?

It can fetch recent logs on demand but does not stream logs in real time.

How do I deploy this MCP server?

Deployment typically involves running the server in an environment with AWS credentials and network access to CloudWatch.

What troubleshooting capabilities does it enable?

It allows AI models to correlate and analyze logs for root cause analysis and incident resolution.