Wazuh MCP Server

Talk to your SIEM. Query alerts, hunt threats, check vulnerabilities, and trigger active responses across your entire Wazuh deployment — through natural conversation with any AI assistant.

v4.2.1 | 54 security tools | Wazuh 4.8.0–4.14.4 | Changelog

What This Does

Your Wazuh SIEM generates thousands of alerts, vulnerability findings, and agent events daily. Investigating them means juggling dashboards, writing API queries, and manually correlating data across tools.

This MCP server turns that workflow into a conversation:

You:    "Show me critical alerts from the last hour"
AI:     [calls get_wazuh_alerts] Found 3 critical alerts:
        1. SSH brute force from 10.0.1.45 → agent-003 (Rule 5712, Level 10)
        2. Rootkit detection on agent-007 (Rule 510, Level 12)
        3. FIM change /etc/shadow on agent-001 (Rule 550, Level 10)

You:    "Block that source IP on agent-003"
AI:     [calls wazuh_block_ip] Blocked 10.0.1.45 via firewall-drop on agent-003.

You:    "Which agents have unpatched critical CVEs?"
AI:     [calls get_wazuh_critical_vulnerabilities] 3 agents with critical vulnerabilities...

It works with Claude Desktop, Open WebUI + Ollama (fully local, air-gapped), mcphost, or any MCP-compliant client.

Works With Cloud AND Local LLMs

This is a standard MCP tool server. It doesn't care what LLM you use — it just executes tools and returns results.

For security teams that can't send SIEM data to cloud APIs (compliance, air-gapped networks, data sovereignty), the local mode with Ollama keeps everything on-premises. Both modes coexist — same server, same tools, same API.

Quick Start: Local LLM with mcphost

# 1. Start the MCP server
docker compose up -d

# 2. Install mcphost (Go binary, no dependencies)
go install github.com/mark3labs/mcphost@latest

# 3. Configure
cat > ~/.mcphost.yml << 'EOF'
mcpServers:
  wazuh:
    type: remote
    url: http://localhost:3000/mcp
    headers: ["Authorization: Bearer ${env://MCP_API_KEY}"]
EOF

# 4. Chat with your SIEM using a local model
export MCP_API_KEY="your-key-from-server-logs"
mcphost --model ollama/qwen2.5:7b

Quick Start: Multi-User SOC with Open WebUI

Open WebUI v0.6.31+ connects to our /mcp endpoint natively. Add it as an MCP tool server in Admin Settings, and your entire team gets AI-powered SIEM analysis with conversation history, RBAC, and a web UI.

54 Security Tools

Every tool is validated, rate-limited, scope-checked, and audit-logged.

The 14 state-changing tools (Active Response + Rollback) require the wazuh:write scope; everything else needs only wazuh:read. ISO 27001 also adds an iso27001_assessment guided prompt (5 prompts total).

Quick Start

Prerequisites

• Docker 20.10+ with Compose v2
• Wazuh 4.8.0–4.14.4 with API access enabled

Deploy

git clone https://github.com/gensecaihq/Wazuh-MCP-Server.git
cd Wazuh-MCP-Server
cp .env.example .env

Edit .env:

WAZUH_HOST=your-wazuh-server
WAZUH_USER=your-api-user
WAZUH_PASS=your-api-password

docker compose up -d
curl http://localhost:3000/health

Connect Claude Desktop

1. Settings → Connectors → Add custom connector
2. URL: https://your-server/mcp
3. Add Bearer token in Advanced settings

Detailed setup: Claude Integration Guide

Security

This server sits between an LLM and your SIEM. Security is not optional.

# Generate a secure API key
python -c "import secrets; print('wazuh_' + secrets.token_urlsafe(32))"

Configuration

Required

Optional

Production note: the server listens over plain HTTP — terminate TLS at a reverse proxy or load balancer. OAuth knobs (OAUTH_ENABLE_DCR — off by default, OAUTH_*_TTL) and rate-limit tuning (RATE_LIMIT_REQUESTS, RATE_LIMIT_WINDOW) are in the Configuration Guide.

Wazuh Indexer (for alert search + vulnerabilities)

Full reference: Configuration Guide

API Endpoints

Architecture

src/wazuh_mcp_server/
├── server.py           # MCP protocol + 54 tool handlers
├── config.py           # Environment-based configuration
├── auth.py             # JWT + API key authentication
├── oauth.py            # OAuth 2.0 with Dynamic Client Registration
├── security.py         # Rate limiting, CORS, input validation
├── monitoring.py       # Prometheus metrics, structured logging
├── resilience.py       # Circuit breakers, retries, graceful shutdown
├── session_store.py    # Pluggable sessions (in-memory + Redis)
└── api/
    ├── wazuh_client.py    # Wazuh Manager REST API client
    └── wazuh_indexer.py   # Wazuh Indexer (Elasticsearch) client

Take It Further: Autonomous Agentic SOC

Combine this MCP server with Wazuh OpenClaw Autopilot to build a fully autonomous Security Operations Center.

While this server gives you conversational access to Wazuh, OpenClaw deploys AI agents that work around the clock — triaging alerts, correlating incidents, and recommending responses without human intervention.

Manual SOC:    Alert → Analyst reviews → Hours → Response
Agentic SOC:   Alert → AI triages → Seconds → Response ready for approval

Explore OpenClaw Autopilot

Documentation

Contributing

We welcome contributions. See Issues for bugs and feature requests, Discussions for questions.

License

MIT

Acknowledgments

• Wazuh — Open source security platform
• Model Context Protocol — AI tool integration standard
• Ollama — Local LLM inference
• Open WebUI — Self-hosted AI chat interface
• mcphost — MCP CLI host with LLM support

Contributors

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