Log Analyzer MCP

Log Analyzer MCP

A Python-based MCP server that enables AI-assisted log file analysis with features for filtering, parsing, and interpreting log outputs, plus executing and analyzing test runs with varying verbosity levels.

Category
Visit Server

README

Log Analyzer MCP

CI codecov PyPI - Version

Overview: Analyze Logs with Ease

Log Analyzer MCP is a powerful Python-based toolkit designed to streamline the way you interact with log files. Whether you're debugging complex applications, monitoring test runs, or simply trying to make sense of verbose log outputs, this tool provides both a Command-Line Interface (CLI) and a Model-Context-Protocol (MCP) server to help you find the insights you need, quickly and efficiently.

Why use Log Analyzer MCP?

  • Simplify Log Analysis: Cut through the noise with flexible parsing, advanced filtering (time-based, content, positional), and configurable context display.
  • Integrate with Your Workflow: Use it as a standalone loganalyzer CLI tool for scripting and direct analysis, or integrate the MCP server with compatible clients like Cursor for an AI-assisted experience.
  • Extensible and Configurable: Define custom log sources, patterns, and search scopes to tailor the analysis to your specific needs.

Key Features

  • Core Log Analysis Engine: Robust backend for parsing and searching various log formats.
  • loganalyzer CLI: Intuitive command-line tool for direct log interaction.
  • MCP Server: Exposes log analysis capabilities to MCP clients, enabling features like:
    • Test log summarization (analyze_tests).
    • Execution of test runs with varying verbosity.
    • Targeted unit test execution (run_unit_test).
    • On-demand code coverage report generation (create_coverage_report).
    • Advanced log searching: all records, time-based, first/last N records.
  • Hatch Integration: For easy development, testing, and dependency management.

Getting Started: Using Log Analyzer MCP

There are two primary ways to use Log Analyzer MCP:

  1. As a Command-Line Tool (loganalyzer):

    • Ideal for direct analysis, scripting, or quick checks.
    • Requires Python 3.9+.
    • For installation and usage, please see the Getting Started Guide.
  2. As an MCP Server (e.g., with Cursor):

    • Integrates log analysis capabilities directly into your AI-assisted development environment.
    • To install and configure the MCP server for use in a client like Cursor, follow the instructions below.

Installing the MCP Server for Client Integration

To integrate the Log Analyzer MCP server with a client application (like Cursor), you'll typically configure the client to launch the log-analyzer-mcp package, which is available on PyPI.

Example Client Configuration (e.g., in .cursor/mcp.json):

{
  "mcpServers": {
    "log_analyzer_mcp_server_prod": {
      "command": "uvx", // uvx is a tool to run python executables from venvs
      "args": [
        "log-analyzer-mcp" // Fetches and runs the latest version from PyPI
        // Or, for a specific version: "log-analyzer-mcp==0.2.0"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1",
        "PYTHONIOENCODING": "utf-8",
        "MCP_LOG_LEVEL": "INFO", // Recommended for production
        // "MCP_LOG_FILE": "/path/to/your/logs/mcp/log_analyzer_mcp_server.log", // Optional
        // --- Configure Log Analyzer specific settings via environment variables ---
        // Example: "LOG_DIRECTORIES": "[\"/path/to/your/app/logs\"]",
        // Example: "LOG_PATTERNS_ERROR": "[\"Exception:.*\"]"
        // (Refer to docs/configuration.md (once created) for all options)
      }
    }
    // You can add other MCP servers here
  }
}

Notes:

  • Replace placeholder paths and consult the Getting Started Guide and Developer Guide for more on configuration options and environment variables.
  • The actual package name on PyPI is log-analyzer-mcp.

Documentation

  • API Reference: Detailed reference for MCP server tools and CLI commands.
  • Getting Started Guide: For users and integrators.
  • Developer Guide: For contributors and those building from source.
  • Refactoring Plan: Technical details on the ongoing evolution of the project.
  • (Upcoming) Configuration Guide: Detailed explanation of all .env and environment variable settings.
  • (Upcoming) CLI Usage Guide: Comprehensive guide to all loganalyzer commands and options.

Contributing

We welcome contributions! Please see CONTRIBUTING.md and the Developer Guide for guidelines on how to set up your environment, test, and contribute.

License

Log Analyzer MCP is licensed under the MIT License with Commons Clause. See LICENSE.md for details.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured