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analyze_timing

Analyze timing patterns to detect beaconing or periodic communication in network traffic. Identify C2 heartbeats and automated callbacks for anomaly detection.

Instructions

Analyze timing patterns to detect beaconing or periodic communication. Use this when: 1) User suspects C2 heartbeat, 2) Looking for automated callbacks, 3) Anomaly detection in time series.

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": {
      "session_id": "<session_id>"
    },
    "name": "analyze_timing"
  }
}

Input Schema

Name	Required	Description
`session_id`	Yes	ID of the session
`interval_seconds`	No	Time interval for grouping
`anomaly_threshold`	No	Standard deviation threshold for anomalies

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 9, 2026

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states that the tool analyzes timing patterns; it does not disclose whether this is read-only, what the output looks like, or how anomalies are determined. The lack of any behavioral detail (e.g., output format, side effects, algorithm specifics) is a significant gap.

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 concise: a single sentence followed by a focused list of three use cases. Every element contributes to understanding the tool's purpose and usage, with no redundant or filler content.

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

Completeness2/5

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

The tool has no output schema, and the description does not explain what the analysis returns (e.g., a list of suspicious intervals, scores, or timestamps). This leaves the agent guessing about the result format. While the purpose and parameters are clear, the missing return value information makes the description incomplete for an agent to use the tool effectively.

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?

The schema covers 100% of the parameters with meaningful descriptions (e.g., 'Time interval for grouping', 'Standard deviation threshold for anomalies'). The tool description adds no additional parameter-level information; the schema already provides the necessary semantics, so a baseline score of 3 is appropriate.

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 action ('Analyze timing patterns') and its purpose ('detect beaconing or periodic communication'). It distinguishes itself from sibling tools like detect_port_scan and analyze_lateral_movement by focusing specifically on timing-based beaconing detection.

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 description provides explicit use cases: when C2 heartbeat is suspected, automated callbacks are sought, or anomaly detection in time series is needed. This gives clear context for when to use the tool, though it does not explicitly list alternatives or when not to use it.

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