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brendanong95

tenable-activity-mcp

by brendanong95

get_actor_profile

Retrieve a full historical profile for any actor, combining fresh audit-log events with previously stored data to show all roles, actions, access types, and IPs across sessions.

Instructions

Full historical view of one actor: roles, actions, access types, IPs.

Combines a fresh audit-log fetch for the actor with everything previously persisted in the local state database, so IPs and actions seen in earlier sessions still show up. The Tenable role is resolved on a best-effort basis from the user directory and is omitted when the API keys cannot list users.

Args: actor_id: The actor UUID (as it appears in actor.id on events). lookback_days: How far back to fetch fresh events for this actor.

Returns: A dict with identity, recent_activity (rollup over the lookback window), lifetime (accumulated state: all IPs, all actions, all access types, first/last seen) and pagination.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actor_idYes
lookback_daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations available, the description carries the behavioral burden and does so well. It discloses that data combines a fresh audit-log fetch with persisted local state, explains best-effort role resolution, and notes when role data is omitted. This is substantive behavioral context beyond a simple summary.

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 well structured with a one-sentence summary, a clear behavioral explanation, and separate Args/Returns sections. It is appropriately detailed for the tool's complexity without padding, and each sentence adds useful information.

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?

Given the tool's moderate complexity, zero annotation coverage, and 0% schema description coverage, the description supplies everything needed: what the profile contains, how data is combined, parameter semantics, and return structure. The output schema also exists, but the description goes beyond it by explaining the lifetime/recent_activity distinction.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates. actor_id is explained as the actor UUID as seen in actor.id, and lookback_days is defined as how far back to fetch fresh events. Both parameters receive meaningful semantic detail not present in the schema.

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 opens with 'Full historical view of one actor: roles, actions, access types, IPs', which names a specific resource and scope. It clearly distinguishes this from siblings like list_activity_events or summarize_activity by emphasizing the per-actor, history-combining nature.

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

Usage Guidelines3/5

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

Usage context is implied rather than explicit: the tool is for obtaining a comprehensive actor profile that merges fresh data with persisted state. It does not explicitly state when to prefer this over sibling tools or provide exclusions, so an agent must infer the right selection.

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