wazuh-daily-triage

rule_timeline

Read-only

Visualize the time distribution of matching Wazuh alerts with date histograms, so you can check when a rule fires before interpreting its overall volume.

Instructions

Time distribution (date_histogram) of matching alerts. Use it BEFORE interpreting a rule's volume.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "rule_timeline"
  }
}

Input Schema

Name	Required	Description
`to`	No	End: ISO-8601 or now. Default now
`env`	No	environment id (list_environments)
`from`	No	Start: ISO-8601 or OpenSearch date math (now-24h). Default now-24h
`agent`	No	Agent name (agent.name) filter
`query`	No	Optional Lucene query_string, e.g. data.srcip:192.0.2.10 AND rule.groups:sshd
`interval`	No	10m, 1h, 1d
`rule_ids`	No	Rule ID filter
`min_level`	No	Minimum rule.level

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.10.1Nov 10, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds that output is bucketed over time rather than raw alerts, which is real value, but says nothing about bucket limits, empty-bucket behavior, or result size. With annotations carrying safety, this is adequate but thin.

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?

Two short sentences, zero filler, with the core purpose stated first and the usage advice second. Nothing could be removed without losing information.

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

Completeness3/5

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

Eight optional parameters and no output schema means the description should at least sketch what comes back (time buckets with counts) or how filters interact with the histogram. It gestures at the shape via 'date_histogram' but leaves the return structure and aggregation behavior largely implicit.

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 100%, so every parameter including from/to defaults, interval formats, and the Lucene query_string is already documented in the schema. The description adds no parameter meaning beyond that, which is the correct baseline when the schema does the work.

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

Purpose4/5

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

States a specific operation and resource: a date_histogram time distribution of matching alerts. The parenthetical names the underlying aggregation, which is concrete. It falls short of 5 because it does not distinguish itself from the plausible sibling aggregate_alerts, which an agent could easily confuse it with.

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?

Gives one piece of sequencing advice -- call this before interpreting a rule's volume -- which is genuinely useful ordering guidance. It offers no when-not-to-use condition, no named alternative, and no prerequisite context, leaving the agent to infer the rest.

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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