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Lex6won
by Lex6won

suggest_fix

Returns a safe, public-official-friendly fix plan for a scan finding when you ask how to fix it by rule_id, including plain title and auto-fix eligibility.

Instructions

발견 사항(finding)에 대한 공무원 친화적 안전 수정 방향을 반환합니다.

스캔 결과의 특정 finding을 "어떻게 고치죠?"라고 물을 때 rule_id로 호출하세요. 쉬운 제목(plain_title), 안전한 수정 방향(safe_fix), 자동 수정 가능 여부(can_auto_fix)를 돌려줍니다. 네트워크 호출이 없으며, 자동 수정은 diff 미리보기 후 담당자 확인을 거쳐 적용해야 합니다.

Input Schema

NameRequiredDescriptionDefault
rule_idYes스캔 결과 finding.rule_id 값 (예: NIS-AI-M01)
unsafe_codeNo문제가 된 코드 조각(선택). 넣으면 마스킹된 미리보기가 함께 반환됨

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.5.4
    • addedInput schema / properties / rule_id / description
      Added value: +"스캔 결과 finding.rule_id 값 (예: NIS-AI-M01)"
    • addedInput schema / properties / unsafe_code / description
      Added value: +"문제가 된 코드 조각(선택). 넣으면 마스킹된 미리보기가 함께 반환됨"
  2. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and it discloses real behavioral traits: no network calls, and auto-fix must go through diff preview plus human confirmation before applying. These are the kind of operational facts an agent needs. It does not mention authentication or rate-limit behavior, keeping it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The structure is well front-loaded: purpose, then when to call, then return fields, then the safety caveat. Each sentence carries distinct information with minimal redundancy. It is slightly verbose in re-listing return fields that the output schema already covers.

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?

An output schema exists, so return values need not be explained, yet the description usefully still names them. Given the absence of annotations, the coverage of no-network behavior and the confirmation-gated auto-fix path makes this complete enough for correct invocation.

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 the schema already documents rule_id and the optional unsafe_code with its masking-preview behavior. The description adds the calling convention (invoke by rule_id) but contributes little parameter meaning beyond what the schema provides, matching the baseline 3.

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 states a specific verb and resource: it returns a safe fix direction for a finding, and it enumerates the returned fields (plain_title, safe_fix, can_auto_fix). This clearly separates it from scanner siblings like scan_code or get_rule, though it never names a sibling explicitly to reinforce the distinction.

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?

It gives an explicit trigger: call with rule_id when asking 'how do I fix this?' for a specific finding. That is clear context for invocation. It omits any when-not guidance or comparison to alternatives such as get_rule, so it stops just short of full routing clarity.

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