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seonmikii

gdc-claude-plugin

by seonmikii

list_task_comments

Fetches task comments and mentions, returning the most recent up to 20 in time order. Provides plain-text content, author, edit status, and creation time.

Instructions

태스크의 댓글(멘션) 목록을 조회한다. 최신순 상위 limit개를 시간순(오래된→최신)으로 반환.

서버 페이지네이션(PAGE_SIZE=20, page_size 미지원)상 한 요청으로 최대 20개만 받는다 — limit>20을 줘도 20개까지만 반환된다(가장 최근 댓글 우선). count는 태스크의 전체 댓글 수.

각 댓글: id, author_name(작성자 실명), text(HTML을 벗긴 평문), is_edited(수정됨 여부), created_at.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
task_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses important behavioral traits: server-side pagination caps responses at 20 even if limit exceeds 20, the ordering is latest-selected but returned oldest-first, and count represents total comments. It also describes the output fields, including that text is plain HTML-stripped. This is extensive and goes beyond basic expectations.

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 longer than average but every sentence provides necessary information: purpose, pagination cap, sorting behavior, count meaning, and field list. The structure is clear and efficient, with no filler content.

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?

The output schema already covers return types, so the description need not elaborate on that. It adds critical non-obvious context: pagination cap, ordering quirks, count semantics, and HTML stripping. For a simple listing tool, this is fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no descriptions (0% coverage), so the description compensates by explaining the limit parameter's meaning (top recent N, but capped at 20). However, task_id's semantics are only implied through the phrase 'task's comments' and not explicitly defined, leaving a minor gap.

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 function: 'Retrieves the list of comments (mentions) of a task.' It also specifies key behaviors such as sorting and pagination, which distinguishes it from sibling comment manipulation tools (add/update/delete) and other listing tools.

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 usage as the read-only tool for listing task comments, but it does not explicitly state when to use it instead of alternatives like list_my_mentions or how to decide against other comment-related tools. No exclusions or alternative recommendations are provided, so 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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