About Eventsight
Eventsight is an MCP server published by jonny-jhnson in the Developer Tools category: aI-powered Windows Event Log analyzer that learns from your feedback. Uses Claude AI with RAG to detect suspicious activity, improve accuracy over time, and share learnings across your team. CLI and MCP server interfaces.
The repository has 38 stars and 10 forks, with the last commit 10 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/jonny-jhnson/EventSightThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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Security checks
Static checks derived from the repository 5 days ago. Heuristics on the source, not an audit.
- ✓README presentThe repository ships documentation.
- ✓License declaredUsable under a stated license.
- !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
- ✓Dependencies pinnedA lockfile pins dependency versions.
- !No dynamic code executionSource scan found eval or shell execution. Review how tool inputs reach it before granting credentials.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
README
EventSight
AI-powered Windows Event Log analyzer that learns from analyst feedback.
Project Structure
This repository contains two related projects:
EventSight/
├── Eventsight/ # Standalone CLI tool
│ └── data/learnings/ # Shared learnings database
└── Eventsight-MCP/ # MCP server for Claude Code integration
└── .mcp.json # MCP configuration
Why Two Projects?
| Eventsight | Eventsight-MCP | |
|---|---|---|
| Interface | Command-line (CLI) | MCP Server (Claude Code) |
| RAG Type | Standard RAG | Agentic RAG |
| Use Case | Direct analysis & reporting | Interactive analysis with Claude |
| Feedback | Manual commands | Natural language via Claude |
Shared Resources
Both projects share the same learnings database, meaning:
- Learnings created in either project benefit both
- Correlation rules are shared
- Analysis improvements compound over time
The shared data lives in Eventsight/data/learnings/:
learnings.db- Analyst learnings, correlation rules, and analysis historyevents.db- Stored events from analyses (indexed by Event ID, timestamp, provider)embeddings.npy- 384-dimensional vector embeddings for semantic searchevent_embeddings.npy- Event embeddings for semantic event search
Eventsight (CLI)
The standalone command-line tool for Windows Event Log analysis.
Features:
- Parse and analyze EVTX files
- Batch processing with streaming output
- Continuous monitoring mode with live HTML report (auto-refreshing dashboard)
- Standard RAG for fast, deterministic learning retrieval
- Interactive feedback mode
- HTML/Markdown report generation
Quick Start:
cd Eventsight
uv sync
uv run eventsight evaluate Security.evtx --event-ids 4624,4688
See Eventsight/README.md for full documentation.
Eventsight-MCP (Claude Code Integration)
MCP server that brings EventSight capabilities into Claude Code with Agentic RAG.
Features:
- All EventSight analysis capabilities via 16 MCP tools
- Agentic RAG for intelligent feedback processing
- Natural language interaction ("Finding #2 is our EDR, mark as benign")
- Semantic event search
- Export/import learnings for sharing between instances
Quick Start:
# From the Eventsight-MCP directory with Claude Code
cd Eventsight-MCP
# Then use tools like:
# - evaluate_evtx to analyze EVTX files
# - feedback to process analyst input using Agentic RAG
# - search_stored_events to find specific activity
# - export_learnings_package to share learnings
See Eventsight-MCP/README.md for full documentation.
RAG Approaches
Standard RAG (Eventsight)
Direct vector similarity search for learning retrieval:
- Query → Embedding → Similarity Search → Top-K Results
- Fast O(1) lookup by Event ID when available
- Deterministic, efficient for batch processing
Agentic RAG (Eventsight-MCP)
LLM-driven tool selection for intelligent processing:
- Query → LLM analyzes intent → Selects appropriate tools
- Can search events, create learnings, mark findings autonomously
- Better at understanding nuanced natural language feedback
- Creates higher-quality, generalizable learnings
License
MIT
README fetched from the repository 5 days ago. The original is authoritative.