claroty-ot-topology

name	claroty-ot-topology
description	Render Claroty xDome OT / IoT communication maps and zone segmentation as inline Canvas/A2UI topology, draw.io diagrams, and timeline summaries.
license	Apache-2.0
user-invocable	true
metadata	{"openclaw":{"requires":{"bins":"[Truncated]","env":"[Truncated]"}}}

Claroty OT Topology

Visualise the OT / IoT communication fabric observed by Claroty xDome — device-to-device edges, organisation zones, and OT activity timelines — using Canvas / A2UI for inline chat rendering and draw.io for exportable diagrams.

When to Use

• Visualising how OT devices communicate (PLC ↔ HMI, RTU ↔ historian, etc.)
• Reviewing network segmentation zones and verifying Purdue-layer separation
• Producing an OT topology diagram for a CR or QBR deck
• Walking an activity timeline for a specific device during incident review
• Sanity-checking that newly deployed zones are reflected in observed traffic

MCP Server

• Server: claroty-mcp
• Command: python3 -u mcp-servers/claroty-mcp/claroty_mcp_server.py (stdio transport)
• Auth: Bearer token via CLAROTY_API_TOKEN
• ITSM: This is a read-only skill — no ITSM gate required.

Available Tools

Tool	Parameters	What It Does
get_device_communication_map	device_id?, site_id?, limit?, offset?	Device-to-device edges (src, dst, protocol, port, byte counts)
list_organization_zones	limit?, offset?	Network segmentation zones (id, name, device count)
list_ot_activity_events	device_id?, site_id?, event_type?, start?, end?, limit?, offset?, max_items?	OT activity / protocol observations
list_devices	(see claroty-asset-inventory)	Resolve device IDs ↔ human-friendly names for diagram labels

Compose with:

• canvas-network-viz skill for inline Canvas / A2UI topology rendering
• drawio- skill for exportable .drawio / SVG diagrams
• uml- skill for nwdiag-style topology

Workflow Examples

Inline topology for a device

"Show me the communication map for device 7a2c... as an inline topology"

1. get_device_communication_map(device_id="7a2c...") → edges.
2. list_devices(...) to resolve neighbour device IDs to names.
3. Hand-off to canvas-network-viz to render the topology in chat with health-coloured nodes.

Zone segmentation audit

"List all xDome zones and show their device counts"

Calls list_organization_zones(). The agent then optionally calls list_devices per zone to surface devices that should be in a stricter zone but aren't.

Site-wide topology export

"Render a draw.io diagram of the OT topology at site warehouse-east"

1. list_devices(site_id="warehouse-east") → nodes.
2. get_device_communication_map(site_id="warehouse-east") → edges.
3. Hand off to the draw.io skill to produce an exportable .drawio file.

OT activity timeline during incident review

"Show me OT activity events for device 7a2c between 09:00 and 11:00 today"

Calls list_ot_activity_events(device_id="7a2c...", start="2026-06-08T09:00:00Z", end="2026-06-08T11:00:00Z"). Optionally renders as a Canvas A2UI timeline.

Rendering hand-offs

This skill is intentionally a data + composition skill — it does not own the rendering layer. For visual output:

• Inline chat: canvas-network-viz (topology map A2UI primitive)
• Exportable diagram: drawio- skill or uml- skill (nwdiag)
• Timeline: canvas-network-viz (timeline A2UI primitive)

This keeps Principle VII (Skill Modularity) intact — Claroty-specific knowledge stays here, generic rendering lives in the visualisation skills.