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Tenable OT Security MCP Server

Tenable OT Security MCP Server

by aqamahn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get communication paths for an asset

get_communication_paths

Discover an OT asset's L2 communication paths, listing peer IDs, protocols, and conversation timings for attack-path analysis.

Instructions

Returns observed L2 communication links involving one OT asset, with peer asset id, protocols seen, traffic / conversation count, and first/last-conversation times. Peer assets are returned as IDs only — call get_asset on each peer id to enrich with name / vendor / type. The consuming AI uses this as the graph adjacency for attack-path reasoning; call again on a peer's id to expand further.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "asset_id": "<asset_id>"
    },
    "name": "get_communication_paths"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
sinceNo
asset_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral disclosure burden. It discloses key traits: returns only peer IDs (not full asset data), requires follow-up calls to get_asset for enrichment, and supports recursive traversal. While it doesn't mention rate limits or pagination, the disclosed constraints are significant and actionable for an AI agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each earning its place: the first states the core function, the second explains the ID-only output and its enrichment path, and the third contextualizes recursive use. No filler or redundancy; front-loaded with the action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists, return details are covered. The description provides strong context: graph adjacency usage, peer ID enrichment, and recursive expansion. It falls short only in explaining all input parameters, but for an overall tool with moderate complexity, it is nearly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It implies asset_id via 'involving one OT asset' and the tool name, but never explicitly names the parameter. The limit and since parameters are entirely unexplained; the description does not mention limiting results or filtering by time. This leaves meaning gaps for 2 of 3 parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Returns observed L2 communication links involving one OT asset, with peer asset id, protocols seen, traffic / conversation count, and first/last-conversation times.' This specific verb+resource combination distinguishes it from siblings like get_asset, which returns asset details, and query_assets, which handles broader search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly guides when to use this tool: 'The consuming AI uses this as the graph adjacency for attack-path reasoning; call again on a peer's id to expand further.' It also names an alternative for enrichment: 'call `get_asset` on each peer id to enrich with name / vendor / type.' This clearly frames usage in relation to alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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