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jbnu-lms

jbnu-lms

by moon0825
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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LMS MCP 기능 제안

suggest_lms_feature

Collects feature or UX improvement suggestions after user confirmation, excluding personal and LMS data, and transmits to the configured remote endpoint or queues locally for retry.

Instructions

새 기능이나 UX 개선 의견을 접수합니다. 호출 전 사용자에게 전송할 내용을 보여 주고 명시적 승인을 받아야 합니다. 개인정보와 LMS 학업 내용은 수집하지 않습니다. 원격 수집 주소가 설정되면 즉시 전송하고 실패하면 로컬에 재시도 대기 상태로 보관합니다.

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": {
      "summary": "<summary>",
      "details": "<details>",
      "confirm_submit": "<confirm_submit>"
    },
    "name": "suggest_lms_feature"
  }
}

Input Schema

NameRequiredDescriptionDefault
detailsYes사용자가 확인한 상세 설명 (10~4000자). 개인정보와 LMS 본문은 넣지 않음
summaryYes사용자가 확인한 한 줄 요약 (5~160자)
affected_toolNo문제가 발생했거나 개선할 MCP 도구 이름
diagnostic_idNo오류 응답의 진단 ID
confirm_submitYes사용자가 위 내용을 확인하고 접수·전송을 명시적으로 승인했을 때만 true
expected_behaviorNo기대한 동작 또는 원하는 개선 결과
steps_to_reproduceNo재현 단계. 강좌명·과제명·공지 본문은 익명화
include_technical_contextNo앱 버전, OS 종류, CPU 아키텍처, Node 주버전 포함. 기본 true

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.0

TDQS

A4.3/5.0
Behavior5/5

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

The description discloses meaningful behavioral details beyond the annotations: it sends submitted content to a remote address if configured, requires explicit user consent before calling, avoids collecting personal and LMS academic content, and stores locally in a retry-pending state on failure. No contradiction with annotations exists.

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 and well-structured, consisting of two focused sentences that convey purpose, consent requirement, privacy scope, and failure behavior without unnecessary detail.

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

Completeness5/5

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

Given the rich schema and the absence of an output schema, the description is complete enough for an agent to understand when and how to invoke the tool, what side effects may occur, and what safeguards are required.

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 JSON schema already provides 100% coverage of all 8 parameters with clear descriptions and constraints. The tool description adds overall context about privacy and consent but does not significantly enrich per-parameter semantics beyond what the schema already states.

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 purpose: receiving new feature or UX improvement suggestions. It uses a specific verb ('접수합니다') and a specific resource ('새 기능이나 UX 개선 의견'), which distinguishes it from problem-reporting siblings like report_lms_problem.

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?

The description implies when to use the tool (for feature/UX suggestions) and includes important procedural guidance about showing content and obtaining explicit consent. However, it does not explicitly contrast this tool with alternatives such as report_lms_problem or explain 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.

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