tenable-activity-mcp

detect_anomalies

Compare an activity window against historical baselines to flag unusual events, volume spikes, new sources, and failure bursts. Pinpoints outliers with evidence and threshold reasoning.

Instructions

Compare a window against each actor's stored baseline and flag outliers.

Checks run: previously unseen actor, event volume above SPIKE_MULTIPLIER x the actor's historical daily average, source IPs absent from the baseline lookback, failed-event bursts, sustained high failure rate, off-hours activity spikes, and never-before-seen action types. Every finding includes the observed value, the threshold crossed, and a reasoning sentence.

Baselines live in the local SQLite state file and are refreshed from the period immediately preceding the window when they are stale. Events in the analysed window itself are not folded into the baseline, so re-running the same window yields the same findings.

Args: date_from: Start of the window under investigation, ISO-8601. date_to: End of the window under investigation, ISO-8601. baseline_days: Days of history preceding the window to baseline against.

Returns: A dict with findings (severity-ordered, each with evidence and reasoning), findings_by_type/findings_by_severity counts, and the active thresholds.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`date_to`	Yes
`date_from`	Yes
`baseline_days`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 4, 2026

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations, the description carries full burden and covers side effects (baseline refresh from local SQLite when stale), idempotence (analyzed window not folded into baseline), and output guarantees (evidence, reasoning, severity ordering). This exceeds what schema alone could convey.

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 definition is dense but every sentence adds necessary operational detail; purpose is front-loaded, checks are grouped, and Args/Returns are clearly structured. No filler or repetition.

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?

For a tool with no annotations and moderate input complexity, it explains inputs, output shape, baseline mechanics, staleness handling, and idempotence. An agent has enough to select and call it correctly without further discovery.

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 coverage is 0%, but the Args section documents all three parameters with semantic meaning: ISO-8601 window bounds and baseline history days. This fully compensates for the absent schema descriptions.

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?

Opens with a specific verb-resource pair ('Compare a window against each actor's stored baseline') and an enumerated list of anomaly checks that distinguishes this from sibling event-listing/summary tools. The resource and behavior are unmistakable.

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

Usage Guidelines4/5

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

The intended investigation scenario is clear: detect baseline deviations in a date window using prior history. It does not explicitly name alternatives or when-not-to-use, but the detailed scope leaves little ambiguity about when this tool applies.

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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