About Lacework Mcp Server
Lacework Alerts MCP Server exposes Lacework API v2 alert operations to AI agents and LLM integrations. It supports alert investigation and updates, with local or remote operation and per-call credentials for remote use.
Key features
- 01List and filter alerts
- 02Retrieve alert details and timelines
- 03Inspect alert entities and context
- 04Comment on and close alerts
Use cases
- 01Review alerts within a selected time range
- 02Investigate alert events and related entities
- 03Add a timeline comment or close an alert
MCP support
Provides MCP tools for Lacework API v2 alert operations. It can run locally over stdio or remotely over SSE and Streamable HTTP.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMELacework Alerts MCP Server
An MCP (Model Context Protocol) server built with FastMCP that exposes Lacework API v2 alert operations as tools for AI agents and LLM integrations.
Quick Start (New Machine Setup)
# 1. Clone the repo
git clone <repo-url>
cd lacework_mcp_server
# 2. Create a virtual environment (Python 3.10+)
python3 -m venv .venv
source .venv/bin/activate
# 3. Install dependencies
pip install -e .
# or manually:
# pip install fastmcp httpx
# 4. Configure Lacework credentials (pick one)
# Option A – Config file
cat > ~/.lacework.json <<'EOF'
{
"account": "yourcompany.lacework.net",
"keyId": "YOUR_ACCESS_KEY_ID",
"secret": "YOUR_SECRET_KEY"
}
EOF
# Option B – Environment variables
export LACEWORK_ACCOUNT="yourcompany"
export LACEWORK_KEY_ID="YOUR_ACCESS_KEY_ID"
export LACEWORK_SECRET="YOUR_SECRET_KEY"
# Environment variables take precedence over the config file.
# 5. Run the server
python lacework_mcp_server.py
Tools
| Tool | Description |
|---|---|
list_alerts | List alerts within an optional time range (supports relative times like 2h, last 2 hours) |
search_alerts | Search alerts with filters (severity, status, alert type) and flexible time inputs (30m, last 2 hours, 2024-06-01) |
get_alert_details | Get detailed info for a specific alert (Details, Investigation, Events, RelatedAlerts, Integrations, Timeline, ObservationTimeline) |
get_alert_timeline | Shortcut – get the timeline for an alert |
get_alert_investigation | Shortcut – get investigation details for an alert |
get_alert_entities | List entities (machines, IPs) associated with an alert |
get_alert_entity_details | Get enriched context for a specific entity (VirusTotal, network activity, etc.) |
post_alert_comment | Post a comment on an alert's timeline |
close_alert | Close an alert with a reason code |
Running
Standalone (stdio – local)
source .venv/bin/activate
python lacework_mcp_server.py
Remote (SSE / Streamable HTTP)
Run the server on a remote host so AI agents can connect over HTTP and pass credentials per-request:
# SSE transport (default host 0.0.0.0, port 8000)
python lacework_mcp_server.py --transport sse --port 8000
# Streamable HTTP transport
python lacework_mcp_server.py --transport streamable-http --host 0.0.0.0 --port 9000
When running remotely, callers pass Lacework credentials as tool parameters instead of relying on server-side config:
{
"name": "search_alerts",
"arguments": {
"start_time": "last 2 hours",
"severity": "Critical",
"lacework_account": "mycompany",
"lacework_key_id": "MY_KEY_ID",
"lacework_secret": "MY_SECRET"
}
}
All three credential fields (lacework_account, lacework_key_id, lacework_secret) are optional on every tool. When omitted, the server falls back to its local config (env vars / ~/.lacework.json). Clients for different Lacework accounts are cached so tokens are reused across calls.
With Claude Desktop / VS Code
Add to your MCP settings (e.g. ~/.claude/claude_desktop_config.json or .vscode/mcp.json):
Local (with ~/.lacework.json present):
{
"mcpServers": {
"lacework": {
"command": "/path/to/lacework_mcp_server/.venv/bin/python",
"args": [
"/path/to/lacework_mcp_server/lacework_mcp_server.py"
]
}
}
}
Local (without ~/.lacework.json – pass creds via env):
{
"mcpServers": {
"lacework": {
"command": "/path/to/lacework_mcp_server/.venv/bin/python",
"args": [
"/path/to/lacework_mcp_server/lacework_mcp_server.py"
],
"env": {
"LACEWORK_ACCOUNT": "yourcompany",
"LACEWORK_KEY_ID": "YOUR_KEY_ID",
"LACEWORK_SECRET": "YOUR_SECRET"
}
}
}
}
Remote (server running elsewhere via SSE):
{
"mcpServers": {
"lacework": {
"url": "http://your-server-host:8000/sse"
}
}
}
For remote servers, credentials are passed as tool parameters on each call (
lacework_account,lacework_key_id,lacework_secret).
API Reference
Based on the Lacework API v2 documentation:
- Authentication: Uses
POST /api/v2/access/tokenswith automatic token refresh - Alerts: Full CRUD via
/api/v2/Alertsendpoints - Rate limits: 480 requests/hour per functionality
- Time ranges: Max 7 days per request; default is last 24 hours
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.