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Tenable OT Security MCP Server

Tenable OT Security MCP Server

by aqamahn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Query attack-pathway data (relational, not computed)

query_attack_pathways

Expand an asset's network graph one hop at a time: returns connected peer IDs with protocols and conversation counts, enabling analysis of lateral movement and attack routes.

Instructions

Returns the asset's 1-hop network neighborhood: the asset itself plus a list of peer-asset IDs it has communicated with, with the protocols and conversation count of each link. Use this AS THE GRAPH the AI walks to reason about attack paths — call again on each peer's id to expand further. The server does NOT compute paths, pick highest-risk routes, or score compromise time. That's the AI's job. Peer assets are returned as IDs only — call get_asset on each to enrich with name / vendor / type.

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": {
      "entry_asset_id": "<entry_asset_id>"
    },
    "name": "query_attack_pathways"
  }
}

Input Schema

NameRequiredDescriptionDefault
max_peersNo
entry_asset_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden for behavioral disclosure. It discloses key behaviors: returns the asset itself plus peer IDs, includes protocols and counts, does not compute paths, and returns IDs only requiring enrichment via get_asset. It doesn't mention pagination or max_peers behavior, but the core behavioral traits are well covered.

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?

Three sentences, each earning its place: returns, usage, exclusions and enrichment path. Front-loaded with the core result, then practical guidance, then limitation. No redundancy or filler.

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

Completeness5/5

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

Despite having an output schema (so return-value details are covered elsewhere), the description explains the relational nature, iteration strategy, and enrichment workflow. It gives the AI enough context to use the tool correctly and reason about attack paths, making it complete for its purpose.

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

Parameters3/5

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 implicitly clarifies entry_asset_id by describing the asset's neighborhood, but max_peers is never mentioned. The description adds meaning for the primary parameter but leaves the secondary parameter entirely undocumented, a clear gap.

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 returns the asset's 1-hop network neighborhood with protocols and conversation counts. It explicitly distinguishes itself from path-computation tools by saying the server does NOT compute paths, which differentiates it from siblings like get_communication_paths.

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?

Provides explicit usage guidance: use this as the graph the AI walks, call again on each peer ID to expand, and call get_asset to enrich peer IDs. It also clarifies what not to expect (no path computation) and assigns that responsibility to the AI, making alternatives clear.

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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