AI-powered security operations for Wazuh SIEM—use any MCP-compatible client to ask security questions in plain English. Faster threat detection, incident triage, and compliance checks with real-time monitoring and anomaly spotting. Production-ready MCP server for conversational SOC workflows.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"wazuh-mcp-server": {
"command": "<see-readme>",
"args": []
}
}
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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.
This server supports HTTP transport. Be the first to test it — help the community know if it works.
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
No package registry to scan.
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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 | 48 security tools | Wazuh 4.8.0–4.14.4 | Changelog
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_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.
This is a standard MCP tool server. It doesn't care what LLM you use — it just executes tools and returns results.
| Mode | LLM | Client | Data leaves your network? |
|---|---|---|---|
| Cloud | Claude, GPT, etc. | Claude Desktop, any MCP client | Yes (to LLM provider) |
| Local | Llama, Qwen, Mistral via Ollama | Open WebUI, mcphost, IBM/mcp-cli | No. Fully air-gappable. |
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.
# 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
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.
Every tool is validated, rate-limited, scope-checked, and audit-logged.
| Category | Tools | What They Do |
|---|---|---|
| Alerts (4) | get_wazuh_alerts get_wazuh_alert_summary analyze_alert_patterns search_security_events | Query, filter, search, and analyze alert data via Elasticsearch |
| Agents (6) | get_wazuh_agents get_wazuh_running_agents check_agent_health get_agent_processes get_agent_ports get_agent_configuration | Monitor agent status, running processes, open ports, and configs |
| Vulnerabilities (3) | get_wazuh_vulnerabilities get_critical_vulnerabilities vulnerability_summary | Query CVEs by severity, agent, and package |
| Security Analysis (6) | analyze_security_threat `chec |