rootcause-mcp

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rc_confirm_classification

Confirm an HFACS classification for root cause descriptions, enabling the system to learn from expert decisions and refine future suggestions.

Instructions

Confirm an HFACS classification as correct. This helps the system learn from expert decisions and improve future suggestions. Confirmed classifications are stored as learned rules.

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": {
      "description": "<description>",
      "hfacs_code": "<hfacs_code>",
      "reason": "<reason>"
    },
    "name": "rc_confirm_classification"
  }
}

Input Schema

Name	Required	Description
`description`	Yes	The original cause description
`hfacs_code`	Yes	The confirmed HFACS code (e.g., 'UA-S', 'PC-C-PMC', 'EF-RE')
`reason`	Yes	Brief explanation of why this classification is correct
`session_id`	No	Optional session ID for tracking
`confidence`	No	Confidence level (0.0-1.0)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 26, 2026

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that confirmed classifications are stored as learned rules, which is a key behavioral trait (side effect). This helps the agent understand the learning impact. It could mention irreversibility or permission requirements, but the disclosure is adequate.

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 consists of two concise, front-loaded sentences with no wasted words. Every sentence adds value: action statement, learning purpose, and storage behavior.

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

Completeness4/5

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

Given the lack of output schema, the description does not explain the return value, but the action is simple. It covers the core purpose and key behavior. It could mention that the tool requires a prior suggestion or that the reason parameter is used for traceability, but it is sufficiently complete for a straightforward confirmation tool.

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 100%, so each parameter already has a description. The tool description adds no additional meaning beyond what the schema provides, earning the baseline score of 3.

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 action 'Confirm an HFACS classification as correct' and specifies the resource. It explains the higher-level purpose: helping the system learn and improving future suggestions, distinguishing it from sibling tools like rc_suggest_hfacs and rc_list_learned_rules.

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 implicitly indicates that this tool should be used when a classification needs to be confirmed and stored as a learned rule. It provides context for learning but does not explicitly state when not to use it or mention alternatives. However, given sibling tools, the usage is clear.

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