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wazuh-daily-triage

wazuh-daily-triage

by dururustem68
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

propose_knowledge

Submit a knowledge-base proposal for durable, reusable baselines, decisions, lessons, or rule drafts; nothing applies until the user approves it.

Instructions

Add a PROPOSAL to the knowledge base (baseline, decision, open item, lesson, rule draft). It has no effect until the user approves it. Only for durable, reusable knowledge. For kind=rule_draft put the full XML in xml and two-way test events in tests.

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": {
      "kind": "<kind>",
      "title": "<title>"
    },
    "name": "propose_knowledge"
  }
}

Input Schema

NameRequiredDescriptionDefault
envNoenvironment id (list_environments)
xmlNorule_draft only: complete <group>...</group> XML
bodyNo
kindYes
testsNorule_draft only: logtest events
titleYes
rule_idsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.10.1

TDQS

A3.7/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=false and openWorldHint=false; the description adds the crucial two-phase behavior — the write has no effect until user approval — which materially changes how an agent should treat the result. It stops short of covering permissions, deduplication behavior, or what a caller gets back.

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 doing distinct work: purpose, the approval caveat, the eligibility filter, then the specialized rule_draft requirements. Front-loaded and free of restatement.

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?

With 7 parameters, 43% schema coverage, and no output schema, the description covers the highest-risk path (rule_draft XML plus two-way tests) well. It is still thin on the relational parameters (`rule_ids`), on what `env` selects, and on what a successful proposal returns, so an agent could call it correctly but not confidently explain the outcome.

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 coverage is 43%, below the threshold where the schema carries the load, so the description must compensate. It clarifies `xml` and `tests` for kind=rule_draft and lists most kind values, but leaves `rule_ids`, `env`, `body`, and `title` unaddressed, and omits the `context`/`request` kinds from its parenthetical.

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?

The description gives a specific verb and resource ("Add a PROPOSAL to the knowledge base") and enumerates the proposal kinds it accepts. It never names the `knowledge` sibling to contrast read vs. write, so the distinction is inferable but not stated.

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?

"Only for durable, reusable knowledge" is a real eligibility filter, and the approval caveat tells the agent this is a non-committal step. However, no alternative is named (e.g., when to read existing knowledge first, or when to write directly), so the when-not guidance is implied rather than explicit.

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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