About Log Analyzer with MCP
Log Analyzer with MCP is an MCP server that gives AI assistants access to AWS CloudWatch Logs for analysis, searching and correlation. It is aimed at users who want an assistant to investigate AWS logs. The repository is presented as an example of a client and server working together.
Key features
- 01Browse and search CloudWatch Log Groups
- 02Query logs with CloudWatch Logs Insights syntax
- 03Generate log summaries and identify error patterns
- 04Correlate logs across multiple AWS services
Use cases
- 01Querying AWS logs from Claude during a conversation
- 02Tracing related errors across several AWS services
MCP support
The project is an MCP server that exposes CloudWatch Logs browsing, querying and correlation to AI assistants, with setup notes for Claude for Desktop.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMELog Analyzer with MCP
A Model Context Protocol (MCP) server that provides AI assistants access to AWS CloudWatch Logs for analysis, searching, and correlation.
🏗️ Architecture
🔌 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
🚀 Installation
Prerequisites
- The uv Python package and project manager (includes
uvx) - An AWS account with CloudWatch Logs
- Configured AWS credentials
🚦 Quick Start
-
Make sure to have configured your AWS credentials as described here
-
Update your
claude_desktop_config.jsonfile 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
🔒 Security
See CONTRIBUTING for more information.
📄 License
This project is licensed under the Apache-2.0 License.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.