ascii766164696D

ascii766164696D / log-mcp

Public

MCP server for log file analysis

81
2
100% credibility
Found Feb 28, 2026 at 72 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A helper tool that enables AI assistants to rapidly scan and summarize large log files by highlighting errors and anomalies while ignoring routine entries.

How It Works

1
📖 Discover log helper

You hear about a smart tool that helps your AI friend quickly find problems in huge log files full of computer messages.

2
🛠️ Set it up simply

Your AI installs it for you with one easy instruction, or you follow friendly steps to get it ready on your computer.

3
🔗 Connect to your AI

Link the tool to your AI chat buddy so it gains superpowers for reading and understanding logs.

4
🔍 Feed in a big log

Share a massive log file and ask 'What's wrong here?' – it zips through millions of lines super fast.

5
✨ Spot the key issues

See a short list of real problems like errors, warnings, and surprises, skipping all the everyday noise.

😊 Get clear answers

Your AI explains everything in plain words: the top issues, patterns, and fixes you need to know.

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AI-Generated Review

What is log-mcp?

Log-mcp is a Python MCP server that equips AI tools like Claude or GitHub Copilot with supercharged log analysis. Feed it massive log files—think 67MB Spark dumps or HPC clusters—and it distills errors, anomalies, and patterns into LLM-ready summaries without blowing context windows. Tools like analyze_errors group stack traces by fingerprint, classify_lines flags semantic issues beyond grep ERROR, and compare_logs spots diffs across runs, all via simple MCP calls.

Why is it gaining traction?

Its Rust-powered ML classifier blitzes logs at 1.3M lines/sec, discarding 70-95% noise while catching unwritten "errors" like DNS fails or lease glitches—far smarter than regex. Pairs seamlessly with Claude Code MCP log sessions or GitHub Copilot MCP log in VSCode, turning "what's wrong here?" into plain-English answers. Benchmarks on Loghub datasets prove 99.95% error capture with optional BERT refinement on Apple Silicon.

Who should use this?

DevOps engineers sifting server or Kubernetes logs for outages, SREs diffing GitHub Actions CI fails via mcp github issues, or data teams probing HDFS/Spark anomalies. Ideal for mcp github copilot vscode users automating log mcp analysis in workflows, or n8n/mcp github project manager setups querying mcp server list.

Verdict

Grab it if you're tired of manual grep—solid docs, install scripts for Claude Desktop/mcp github server, and retraining guide make it production-ready despite 38 stars and 1.0% credibility score. Early but battle-tested on real logs; watch for community growth.

(198 words)

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