Shrimp Task Manager
目錄
MCP 새우 작업 관리자

🚀 AI 에이전트를 위한 효율적인 프로그래밍 워크플로 프레임워크를 제공하는 모델 컨텍스트 프로토콜(MCP) 기반의 지능형 작업 관리 시스템입니다.
Shrimp Task Manager는 체계적인 프로그래밍을 위한 구조화된 워크플로를 통해 에이전트를 안내하고, 작업 메모리 관리 메커니즘을 향상시키며, 중복되고 반복적인 코딩 작업을 효과적으로 방지합니다.
Related MCP server: Vibe Coder MCP
✨ 특징
작업 계획 및 분석 : 복잡한 작업 요구 사항에 대한 심층적인 이해 및 분석
지능형 작업 분해 : 대규모 작업을 관리 가능한 소규모 작업으로 자동 분해
종속성 관리 : 작업 간 종속성을 정확하게 처리하여 올바른 실행 순서를 보장합니다.
실행 상태 추적 : 작업 실행 진행 상황 및 상태를 실시간으로 모니터링합니다.
작업 완료성 검증 : 작업 결과가 예상 요구 사항을 충족하는지 확인
작업 복잡성 평가 : 작업 복잡성을 자동으로 평가하고 최적의 처리 제안을 제공합니다.
자동 작업 요약 업데이트 : 작업 완료 시 요약을 자동으로 생성하여 메모리 성능을 최적화합니다.
작업 메모리 기능 : 작업 기록을 자동으로 백업하여 장기 메모리 및 참조 기능을 제공합니다.
연구 모드 : 기술 탐색, 모범 사례 및 솔루션 비교를 위한 가이드 워크플로를 갖춘 체계적인 기술 연구 기능
프로젝트 규칙 초기화 : 대규모 프로젝트에서 일관성을 유지하기 위해 프로젝트 표준 및 규칙을 정의합니다.
웹 GUI : 작업 관리를 위한 웹 기반 그래픽 사용자 인터페이스(GUI)를 선택적으로 제공합니다.
.env파일에서ENABLE_GUI=true설정하여 활성화하세요. 활성화하면 액세스 주소가 포함된WebGUI.md파일이DATA_DIR에 생성됩니다.
🧭 사용 가이드
Shrimp Task Manager는 가이드 워크플로와 체계적인 작업 관리를 통해 AI 지원 프로그래밍에 대한 체계적인 접근 방식을 제공합니다.
새우란 무엇인가?
Shrimp는 AI 에이전트가 프로젝트를 더 잘 이해하고 작업할 수 있도록 안내하는 프롬프트 템플릿입니다. 일련의 프롬프트를 사용하여 에이전트가 프로젝트의 특정 요구 사항과 규칙을 긴밀하게 준수하도록 합니다.
실제 연구 모드
작업 계획에 착수하기 전에, 기술 조사 및 지식 수집을 위해 연구 모드를 활용할 수 있습니다. 특히 다음과 같은 경우에 유용합니다.
새로운 기술이나 프레임워크를 탐색해야 합니다.
다양한 솔루션 접근 방식을 비교하고 싶습니다.
귀하의 프로젝트에 대한 모범 사례를 조사하고 있습니다.
복잡한 기술 개념을 이해해야 합니다.
상담원에게 "[주제]를 조사하세요" 또는 "[기술/문제]에 대한 조사 모드로 전환하세요"라고 말씀하시면 체계적인 조사를 시작할 수 있습니다. 조사 결과는 이후 작업 계획 및 개발 결정에 반영됩니다.
첫 번째 설정
새 프로젝트를 진행할 때는 에이전트에게 "init project rules"라고 알려주기만 하면 됩니다. 이렇게 하면 에이전트가 프로젝트의 특정 요구 사항과 구조에 맞는 규칙 세트를 생성합니다.
작업 계획 프로세스
기능을 개발하거나 업데이트하려면 "plan task [설명]" 명령을 사용하세요. 시스템은 기존에 설정된 규칙을 참조하여 프로젝트를 이해하고, 관련 코드 섹션을 검색하여 프로젝트의 현재 상태를 기반으로 포괄적인 계획을 제안합니다.
피드백 메커니즘
계획 과정에서 Shrimp는 상담원이 여러 단계의 사고 과정을 거치도록 안내합니다. 이 과정을 검토하고 잘못된 방향으로 가고 있다고 생각되면 피드백을 제공할 수 있습니다. 중간에 잠시 멈춰서 의견을 공유해 주세요. 상담원은 피드백을 반영하여 계획 과정을 계속 진행할 것입니다.
작업 실행
계획에 만족하면 "execute task [작업 이름 또는 ID]"를 사용하여 실행하세요. 작업 이름이나 ID를 지정하지 않으면 시스템이 자동으로 우선순위가 가장 높은 작업을 식별하여 실행합니다.
연속 모드
각 작업에 대한 수동 개입 없이 모든 작업을 순서대로 실행하려면 "연속 모드"를 사용하여 전체 작업 대기열을 자동으로 처리합니다.
토큰 제한 사항 참고
LLM 토큰 제한으로 인해 장시간 대화 중에는 문맥이 손실될 수 있습니다. 이 경우 새 채팅 세션을 열고 상담원에게 실행을 계속하도록 요청하세요. 시스템은 작업 세부 정보나 문맥을 다시 입력할 필요 없이 중단된 부분부터 다시 시작합니다.
신속한 언어 및 사용자 정의
TEMPLATES_USE 환경 변수를 설정하여 시스템 프롬프트 언어를 전환할 수 있습니다. 기본적으로 en (영어)과 zh (중국어 번체)를 지원합니다. 또한 기존 템플릿 디렉터리(예: src/prompts/templates_en )를 DATA_DIR 에 지정된 위치로 복사하고 수정한 후 TEMPLATES_USE 에 사용자 지정 템플릿 디렉터리 이름을 지정할 수 있습니다. 이렇게 하면 프롬프트를 더욱 세부적으로 사용자 지정할 수 있습니다. 자세한 지침은 다음을 참조하세요.
🔬 연구 모드
Shrimp Task Manager에는 체계적인 기술 조사와 지식 수집을 위해 설계된 특수 연구 모드가 포함되어 있습니다.
연구 모드란 무엇인가요?
연구 모드는 AI 에이전트가 철저하고 체계적인 기술 연구를 수행할 수 있도록 지원하는 가이드 워크플로 시스템입니다. 기술 탐색, 솔루션 비교, 모범 사례 조사, 프로그래밍 작업을 위한 포괄적인 정보 수집을 위한 체계적인 접근 방식을 제공합니다.
주요 특징
체계적인 조사 : 구조화된 워크플로를 통해 연구 주제에 대한 포괄적인 적용이 보장됩니다.
다중 소스 연구 : 완전한 이해를 위해 웹 검색과 코드베이스 분석을 결합합니다.
상태 관리 : 여러 세션에 걸쳐 연구 맥락과 진행 상황을 유지합니다.
가이드 탐색 : 연구가 초점이 맞지 않거나 주제에서 벗어나는 것을 방지합니다.
지식 통합 : 연구 결과를 작업 계획 및 실행과 원활하게 통합합니다.
연구 모드를 사용할 때
연구 모드는 다음과 같은 경우에 특히 유용합니다.
기술 탐색 : 새로운 프레임워크, 라이브러리 또는 도구 조사
모범 사례 연구 : 업계 표준 및 권장 접근 방식 찾기
솔루션 비교 : 다양한 기술적 접근 방식 또는 아키텍처 평가
문제 조사 : 복잡한 기술적 과제에 대한 심층 분석
아키텍처 계획 : 디자인 패턴 및 시스템 아키텍처 연구
연구 모드 사용 방법
에이전트에게 주제와 함께 연구 모드로 들어가라고 말하기만 하면 됩니다.
기본 사용법 : "[주제]에 대한 연구 모드로 들어가세요"
특정 연구 : "[특정 기술/문제] 연구"
비교 분석 : "[옵션 A 대 B]를 조사하고 비교하세요"
이 시스템은 에이전트를 체계적인 조사 단계로 안내하여 귀하의 특정 요구 사항에 집중하는 동시에 철저한 조사를 보장합니다.
연구 워크플로
주제 정의 : 연구 범위와 목표를 명확하게 정의합니다.
정보 수집 : 관련 정보의 체계적인 수집
분석 및 종합 : 결과 처리 및 구성
상태 업데이트 : 정기적인 진행 상황 추적 및 컨텍스트 보존
통합 : 연구 결과를 프로젝트 컨텍스트에 적용
💡 추천 : 최고의 연구 모드 경험을 위해, 뛰어난 분석 기능과 포괄적인 연구 합성을 제공하는 Claude 4 Sonnet을 사용하는 것을 추천합니다.
🧠 작업 메모리 기능
Shrimp Task Manager는 장기 메모리 기능을 갖추고 있어 작업 실행 내역을 자동으로 저장하고 새로운 작업을 계획할 때 참고 경험을 제공합니다.
주요 특징
시스템은 자동으로 작업을 메모리 디렉토리에 백업합니다.
백업 파일은 task_backup_YYYY-MM-DDThh-mm-ss.json 형식으로 시간순으로 이름이 지정됩니다.
작업 계획 에이전트는 메모리 기능을 사용하는 방법에 대한 지침을 자동으로 받습니다.
장점 및 혜택
중복 작업 방지 : 이전 작업을 참조하고 유사한 문제를 처음부터 해결할 필요가 없습니다.
성공적인 경험에서 배우기 : 검증된 효과적인 솔루션을 활용하고 개발 효율성을 개선하세요
학습 및 개선 : 과거 실수나 비효율적인 솔루션을 식별하고 워크플로를 지속적으로 최적화합니다.
지식 축적 : 시스템 사용량이 증가함에 따라 지속적으로 확장되는 지식 기반을 형성합니다.
업무 메모리 기능을 효과적으로 활용함으로써 시스템은 지속적으로 경험을 축적할 수 있으며, 지능 수준과 업무 효율성이 지속적으로 향상됩니다.
📋 프로젝트 규칙 초기화
프로젝트 규칙 기능은 코드베이스 전반의 일관성을 유지하는 데 도움이 됩니다.
개발 표준화 : 일관된 코딩 패턴과 관행을 확립합니다.
신규 개발자 온보딩 : 프로젝트 기여에 대한 명확한 지침 제공
품질 유지 : 모든 코드가 확립된 프로젝트 표준을 충족하는지 확인하세요.
⚠️ 권장 사항 : 프로젝트 규모가 커지거나 상당한 변경 사항이 발생할 경우 프로젝트 규칙을 초기화하세요. 이렇게 하면 복잡성이 증가하더라도 일관성과 품질을 유지하는 데 도움이 됩니다.
다음과 같은 경우 init_project_rules 도구를 사용하여 프로젝트 표준을 설정하거나 업데이트하세요.
새로운 대규모 프로젝트 시작
새로운 팀원 온보딩
주요 아키텍처 변경 사항 구현
새로운 개발 관례 채택
사용 예
간단한 자연어 명령을 사용하면 이 기능에 쉽게 액세스할 수 있습니다.
초기 설정의 경우 : 에이전트에게 "init rules" 또는 "init project rules"라고 알려주기만 하면 됩니다.
업데이트 : 프로젝트가 진행되면 에이전트에게 "규칙 업데이트" 또는 "프로젝트 규칙 업데이트"를 알려주세요.
이 도구는 코드베이스가 확장되거나 상당한 구조적 변경이 발생할 때 특히 유용하며, 프로젝트 수명 주기 전체에서 일관된 개발 관행을 유지하는 데 도움이 됩니다.
📚 문서 리소스
프롬프트 사용자 정의 가이드 : 환경 변수를 통해 도구 프롬프트를 사용자 정의하기 위한 지침
변경 로그 : 이 프로젝트의 모든 주요 변경 사항 기록
🔧 설치 및 사용
Smithery를 통해 설치
Smithery 를 통해 Claude Desktop용 Shrimp Task Manager를 자동으로 설치하려면:
지엑스피1
수동 설치
# Install dependencies
npm install
# Build and start service
npm run build🔌 MCP 호환 클라이언트와 함께 사용
Shrimp Task Manager는 Cursor IDE와 같이 모델 컨텍스트 프로토콜을 지원하는 모든 클라이언트와 함께 사용할 수 있습니다.
커서 IDE의 구성
Shrimp Task Manager는 글로벌 구성과 프로젝트별 구성이라는 두 가지 구성 방법을 제공합니다.
글로벌 구성
Cursor IDE 글로벌 구성 파일을 엽니다(일반적으로
~/.cursor/mcp.json에 위치).mcpServers섹션에 다음 구성을 추가합니다.
{
"mcpServers": {
"shrimp-task-manager": {
"command": "node",
"args": ["/mcp-shrimp-task-manager/dist/index.js"],
"env": {
"DATA_DIR": "/path/to/project/data", // 必須使用絕對路徑
"TEMPLATES_USE": "en",
"ENABLE_GUI": "false"
}
}
}
}
or
{
"mcpServers": {
"shrimp-task-manager": {
"command": "npx",
"args": ["-y", "mcp-shrimp-task-manager"],
"env": {
"DATA_DIR": "/mcp-shrimp-task-manager/data",
"TEMPLATES_USE": "en",
"ENABLE_GUI": "false"
}
}
}
}⚠️
/mcp-shrimp-task-manager실제 경로로 바꿔주세요.
프로젝트별 구성
또한 각 프로젝트에 대해 전용 구성을 설정하여 다양한 프로젝트에 대해 독립적인 데이터 디렉터리를 사용할 수 있습니다.
프로젝트 루트에
.cursor디렉토리를 만듭니다.다음 내용으로 이 디렉토리에
mcp.json파일을 만듭니다.
{
"mcpServers": {
"shrimp-task-manager": {
"command": "node",
"args": ["/path/to/mcp-shrimp-task-manager/dist/index.js"],
"env": {
"DATA_DIR": "/path/to/project/data", // Must use absolute path
"TEMPLATES_USE": "en",
"ENABLE_GUI": "false"
}
}
}
}
or
{
"mcpServers": {
"shrimp-task-manager": {
"command": "npx",
"args": ["-y", "mcp-shrimp-task-manager"],
"env": {
"DATA_DIR": "/path/to/project/data", // Must use absolute path
"TEMPLATES_USE": "en",
"ENABLE_GUI": "false"
}
}
}
}⚠️ 중요 구성 참고 사항
DATA_DIR 매개변수 는 Shrimp Task Manager가 작업 데이터, 대화 로그 및 기타 정보를 저장하는 디렉터리입니다. 이 매개변수를 올바르게 설정하는 것은 시스템의 정상적인 작동에 매우 중요합니다. 이 매개변수는 절대 경로를 사용해야 합니다. 상대 경로를 사용하면 시스템이 데이터 디렉터리를 잘못 찾아 데이터 손실 또는 기능 장애가 발생할 수 있습니다.
경고 : 상대 경로를 사용하면 다음과 같은 문제가 발생할 수 있습니다.
데이터 파일을 찾을 수 없어 시스템 초기화에 실패했습니다.
작업 상태 손실 또는 올바르게 저장할 수 없음
다양한 환경에서 일관되지 않은 애플리케이션 동작
시스템 충돌 또는 시작 실패
🔧 환경 변수 구성
Shrimp Task Manager는 환경 변수를 통해 프롬프트 동작을 사용자 지정할 수 있도록 지원하므로 코드를 수정하지 않고도 AI 어시스턴트의 응답을 세부적으로 조정할 수 있습니다. 이러한 변수는 구성 파일이나 .env 파일을 통해 설정할 수 있습니다.
{
"mcpServers": {
"shrimp-task-manager": {
"command": "node",
"args": ["/path/to/mcp-shrimp-task-manager/dist/index.js"],
"env": {
"DATA_DIR": "/path/to/project/data",
"MCP_PROMPT_PLAN_TASK": "Custom planning guidance...",
"MCP_PROMPT_EXECUTE_TASK_APPEND": "Additional execution instructions...",
"TEMPLATES_USE": "en",
"ENABLE_GUI": "false"
}
}
}
}두 가지 사용자 정의 방법이 있습니다.
오버라이드 모드 (
MCP_PROMPT_[FUNCTION_NAME]): 기본 프롬프트를 완전히 대체합니다.추가 모드 (
MCP_PROMPT_[FUNCTION_NAME]_APPEND): 기존 프롬프트에 콘텐츠 추가
또한, 다른 시스템 구성 변수도 있습니다.
DATA_DIR : 작업 데이터가 저장되는 디렉토리를 지정합니다.
TEMPLATES_USE : 프롬프트에 사용할 템플릿 세트를 지정합니다. 기본값은
en입니다. 현재 사용 가능한 옵션은en과zh입니다. 사용자 지정 템플릿을 사용하려면src/prompts/templates_en디렉터리를DATA_DIR로 지정된 위치로 복사하고, 복사된 디렉터리의 이름을 변경(예:my_templates)한 후TEMPLATES_USE새 디렉터리 이름(예:my_templates)으로 설정합니다.
지원되는 매개변수와 예를 포함하여 프롬프트 사용자 정의에 대한 자세한 지침은 프롬프트 사용자 정의 가이드를 참조하세요.
💡 시스템 프롬프트 안내
커서 IDE 구성
커서 설정 => 기능 => 사용자 정의 모드를 활성화하고 다음 두 가지 모드를 구성할 수 있습니다.
작업 계획자 모드
You are a professional task planning expert. You must interact with users, analyze their needs, and collect project-related information. Finally, you must use "plan_task" to create tasks. When the task is created, you must summarize it and inform the user to use the "TaskExecutor" mode to execute the task.
You must focus on task planning. Do not use "execute_task" to execute tasks.
Serious warning: you are a task planning expert, you cannot modify the program code directly, you can only plan tasks, and you cannot modify the program code directly, you can only plan tasks.TaskExecutor 모드
You are a professional task execution expert. When a user specifies a task to execute, use "execute_task" to execute the task.
If no task is specified, use "list_tasks" to find unexecuted tasks and execute them.
When the execution is completed, a summary must be given to inform the user of the conclusion.
You can only perform one task at a time, and when a task is completed, you are prohibited from performing the next task unless the user explicitly tells you to.
If the user requests "continuous mode", all tasks will be executed in sequence.💡 귀하의 필요에 따라 적절한 모드를 선택하세요:
작업을 계획할 때 TaskPlanner 모드를 사용하세요.
작업을 실행할 때 TaskExecutor 모드를 사용하세요.
다른 도구와 함께 사용
도구가 사용자 정의 모드를 지원하지 않는 경우 다음을 수행할 수 있습니다.
각 단계에 적절한 프롬프트를 수동으로 붙여넣습니다.
또는
Please plan the following task: ......또는Please start executing the task...와 같은 간단한 명령을 직접 사용하세요.
🛠️ 사용 가능한 도구 개요
구성 후 다음 도구를 사용할 수 있습니다.
범주 | 도구 이름 | 설명 |
작업 계획 |
| 작업 계획을 시작하세요 |
작업 분석 |
| 업무 요구 사항에 대한 심층 분석 |
| 복잡한 문제에 대한 단계별 추론 | |
솔루션 평가 |
| 솔루션 개념을 반영하고 개선합니다. |
연구 및 조사 |
| 체계적인 기술 연구 모드로 전환 |
프로젝트 관리 |
| 프로젝트 표준 및 규칙을 초기화하거나 업데이트합니다. |
작업 관리 |
| 작업을 하위 작업으로 나누세요 |
| 모든 작업 및 상태 표시 | |
| 작업 검색 및 나열 | |
| 완료된 작업 세부 정보 표시 | |
| 완료되지 않은 작업 삭제 | |
작업 실행 |
| 특정 작업 실행 |
| 작업 완료 확인 |
🔧 기술 구현
Node.js : 고성능 JavaScript 런타임 환경
TypeScript : 타입 안전한 개발 환경 제공
MCP SDK : 대규모 언어 모델과의 원활한 상호 작용을 위한 인터페이스
UUID : 고유하고 신뢰할 수 있는 작업 식별자 생성
📄 라이센스
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.
추천 모델
최상의 경험을 위해 다음 모델을 사용하는 것이 좋습니다.
클로드 3.7 : 강력한 이해력과 생성 능력을 제공합니다.
제미니 2.5 : 구글의 최신 모델로 성능이 매우 뛰어납니다.
모델 간 학습 방법과 이해 능력의 차이로 인해, 다른 모델을 사용하면 동일한 질문에 대한 결과가 달라질 수 있습니다. 이 프로젝트는 Claude 3.7과 Gemini 2.5에 최적화되었습니다.
스타 역사
Available Tools
15 toolsanalyze_taskC
深入分析任務需求並系統性檢查代碼庫,評估技術可行性與潛在風險,如果需要提供程式碼請使用 pseudocode 格式且盡量精簡只保留核心實現部分
| Name | Required | Description | Default |
|---|---|---|---|
| summary | Yes | 結構化的任務摘要,包含任務目標、範圍與關鍵技術挑戰,最少10個字符 | |
| initialConcept | Yes | 初步解答構想,包含技術方案、架構設計和實施策略,如果需要提供程式碼請使用 pseudocode 格式且盡量精簡只保留核心實現部分,最少50個字符 | |
| previousAnalysis | No | 前次迭代的分析結果,用於持續改進方案(僅在重新分析時需提供) |
TDQS
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 mentions systematic codebase checking and risk evaluation but does not disclose any behavioral traits such as side effects, required permissions, or state changes. The agent learns the tool's function but not its operational impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that combines purpose and a usage note. It is adequately concise but not front-loaded; the pseudocode instruction could be separate. No wasted words, but structure could be improved.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should clarify what the tool returns. It mentions analysis and codebase checking but does not specify output format. The parameter details are covered by schema. Completeness is adequate but missing return value behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all parameters. The description adds no new meaning beyond the schema, except repeating the pseudocode hint. Baseline score of 3 is appropriate as the schema handles parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: analyzing task requirements, checking codebase, evaluating feasibility and risks. It uses specific verbs like '分析' and '檢查', and it distinguishes from siblings by focusing on analysis and risk assessment, though it could be more explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a guideline to use pseudocode format for code output, but it does not specify when to use this tool instead of siblings like plan_task or reflect_task. There is no explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clear_all_tasksA
刪除系統中所有未完成的任務,該指令必須由用戶明確確認才能執行
| Name | Required | Description | Default |
|---|---|---|---|
| confirm | Yes | 確認刪除所有未完成的任務(此操作不可逆) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a destructive action via 'delete' and a confirmation requirement, but it lacks explicit disclosure of irreversibility or side effects. The parameter description (in schema) adds 'irreversible,' but the main description does not.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the action and includes the confirmation requirement. Every word is purposeful with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple destructive tool with one parameter and no output schema, the description is fairly complete: it states what it does and the confirmation need. It could mention irreversibility explicitly, but the parameter description covers that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers the sole parameter 'confirm' with a description of its role. The main description adds that the command requires user confirmation, which is a slight addition. With 100% schema coverage, baseline is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies 'delete all incomplete tasks,' clearly stating the action and resource. It distinguishes itself from sibling tools like 'delete_task' by targeting all incomplete tasks versus a single task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly requires user confirmation before execution, providing a clear usage condition. However, it does not contrast with alternatives like 'delete_task' for individual tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
complete_taskB
正式標記任務為完成狀態,生成詳細的完成報告,並更新關聯任務的依賴狀態
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | 待標記為完成的任務唯一標識符,必須是狀態為「進行中」的有效任務ID | |
| summary | No | 任務完成摘要,簡潔描述實施結果和重要決策(選填,如未提供將自動生成) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavioral traits. It mentions side effects (report generation, dependency updates) but omits details like permissions, reversibility, or what happens if the task is not in progress. The auto-generation of 'summary' is only in the schema, not repeated here.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, efficient and front-loaded with the verb '正式標記'. It packs three actions without excess, though the density slightly reduces readability. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks information about return values (no output schema) and does not clarify what the agent receives after completion (e.g., the report content or status). Given the complexity of a mutation tool that updates dependencies, more context is needed for the agent to understand the full effect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema: it does not explain taskId format or summary constraints, merely restating the concept of a completion summary. The tool description's mention of 'detailed completion report' is not a parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: marking a task as completed, generating a completion report, and updating dependency status. This distinguishes it from siblings like 'delete_task' or 'update_task', though no explicit sibling differentiation is provided.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description implicitly assumes use for tasks in progress (constraint on taskId), but does not specify when not to use or mention alternative tools like 'update_task' for partial updates.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_taskA
刪除未完成的任務,但不允許刪除已完成的任務,確保系統記錄的完整性
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | 待刪除任務的唯一標識符,必須是系統中存在且未完成的任務ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the key behavioral constraint of only allowing deletion of incomplete tasks, but does not mention other behaviors such as irreversibility, authorization needs, or error handling, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately conveys the core action and constraint with no extraneous words. It is front-loaded and highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers the essential constraint. It could mention return format or error cases, but is otherwise adequate for use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and the schema already provides a detailed description of taskId including the constraint. The tool description adds no new parameter information beyond what the schema states, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Delete incomplete tasks, but does not allow deletion of completed tasks,' which is a specific verb+resource with a scope constraint. It distinguishes from siblings like complete_task and update_task by focusing on deletion of incomplete tasks only.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use (for incomplete tasks) and when not to use (not for completed tasks). However, it does not name alternative tools for handling completed tasks, which would improve guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
execute_taskC
按照預定義計劃執行特定任務,確保每個步驟的輸出符合質量標準
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | 待執行任務的唯一標識符,必須是系統中存在的有效任務ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description lacks details on safety, failure modes, side effects, or whether the tool is idempotent. For a mutation tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is efficient and front-loaded, though it could benefit from a brief usage caveat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of task execution and the presence of 14 sibling tools, the description does not sufficiently distinguish its role or clarify return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear parameter description. The description adds no extra meaning beyond the schema's existing field documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool executes a task according to a predefined plan and ensures quality, but does not differentiate from siblings like complete_task or verify_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as analyze_task, plan_task, or verify_task.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_task_detailA
根據任務ID獲取任務的完整詳細信息,包括未截斷的實現指南和驗證標準等
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | 欲檢視詳情的任務ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It hints at providing untruncated data (implying other tools may truncate), but does not state read-only nature, permissions, or rate limits. Adequate for a simple read operation but lacks explicit behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that front-loads the action and resource. In Chinese, concise but could be restructured for better readability. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers expected content (full details, implementation guide, verification standards). Could specify additional fields like status or timestamps, but sufficient for a get-detail tool among many siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter description '欲檢視詳情的任務ID'. The description adds context about what the returned details include (implementation guide, verification standards), providing value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (獲取) and resource (任務的完整詳細信息), and specifies what includes (未截斷的實現指南和驗證標準). It distinguishes from siblings like list_tasks and update_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when needing full task details including implementation guides, but does not explicitly state when to use vs alternatives like list_tasks or query_task. No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tasksA
生成結構化任務清單,包含完整狀態追蹤、優先級和依賴關係
| Name | Required | Description | Default |
|---|---|---|---|
| status | Yes | 要列出的任務狀態,可選擇 'all' 列出所有任務,或指定具體狀態 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It does not mention that the tool is read-only, has no side effects, or any authorization requirements. The description only covers output structure, leaving behavioral aspects unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that covers the core functionality. It is front-loaded and efficient, though it lacks structural elements like bullet points that might improve scanability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description provides adequate context about the output (status, priority, dependencies). However, it could be improved by noting that the list is filterable by status and clarifying the scope (e.g., all tasks belonging to the current context).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides full description of the status parameter (enum values and meaning). The tool description adds no further parameter semantics beyond showing that the returned list includes additional fields (priority, dependencies), which is about output, not parameter meaning. Baseline 3 is appropriate due to high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a structured task list with status tracking, priority, and dependencies, which precisely defines its purpose. It distinguishes from sibling tools like get_task_detail (single task) and query_task (general query) by focusing on listing with filtering by status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for listing tasks filtered by status but does not explicitly state when to use this tool versus siblings like query_task or analyze_task. No when-not-to-use or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
plan_taskB
初始化並詳細規劃任務流程,建立明確的目標與成功標準,可選擇參考現有任務進行延續規劃
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | 完整詳細的任務問題描述,應包含任務目標、背景及預期成果 | |
| requirements | No | 任務的特定技術要求、業務約束條件或品質標準(選填) | |
| existingTasksReference | No | 是否參考現有任務作為規劃基礎,用於任務調整和延續性規劃 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It mentions establishing goals but does not disclose behavioral traits such as whether the tool modifies state, requires authentication, or has side effects (e.g., creating a plan object). The return value is not mentioned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence in Chinese, covering purpose and optional reference. It is appropriately front-loaded and efficient, though slightly dense due to missing punctuation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and a complex domain (task planning), the description should explain what the tool returns or how it affects state. It lacks information on prerequisites, success criteria format, or the planning output, making it incomplete for agent selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds some context (e.g., '參考現有任務進行延續規劃' for `existingTasksReference`), but does not significantly extend beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it initializes and plans task flow, establishes goals and success criteria, and allows referencing existing tasks. This differentiates it from sibling tools like `execute_task` or `analyze_task`, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for initial planning by mentioning '初始化' and '延續規劃', but does not explicitly state when to use vs. alternatives like `split_tasks` or `update_task`. No 'when not to use' guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
process_thoughtA
你可以透過靈活的、可適應和發展的思考過程來分析問題,隨著理解的加深,每個想法都可以建立、質疑或修改先前的見解。你可以質疑想法、假設想法、驗證想法,並且可以建立新的想法。你將重複這個過程,直到你對問題有足夠的理解,並且能夠提出有效的解決方案。如果你覺得思考已經充分可以把 nextThoughtNeeded 設為 false 並且停止思考。
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | 思維標籤,是一個陣列字串 | |
| stage | Yes | 思考階段,可以選擇的階段有:問題定義、收集資訊、研究、分析、綜合、結論、質疑、規劃 | |
| thought | Yes | 思維內容 | |
| axioms_used | No | 使用的公理,是一個陣列字串 | |
| thought_number | Yes | 當前思維編號 | |
| total_thoughts | Yes | 預計總思維數量 | |
| next_thought_needed | Yes | 是否需要下一步思維 | |
| assumptions_challenged | No | 挑戰的假設,是一個陣列字串 |
TDQS
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 describes the iterative reasoning process but does not disclose potential side effects, logging, or safety implications beyond the cognitive process.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single block of Chinese text that efficiently conveys the tool's purpose and usage, though it could be slightly more condensed. It is front-loaded with the core idea.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 8 parameters (5 required) and no output schema, the description provides a comprehensive overview of the thinking process. It lacks explanation of specific parameter values like stage enums, but schema covers those.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are well-documented. The description does not add additional semantics beyond the process explanation, earning the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: an adaptive thinking process to analyze problems by building, questioning, and modifying insights. It effectively distinguishes from sibling task management tools like 'analyze_task' or 'execute_task' by focusing on internal reasoning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description explains the iterative process and when to stop (set next_thought_needed to false), it does not explicitly state when to use this tool over alternatives, nor does it provide exclusion criteria or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_taskB
根據關鍵字或ID搜尋任務,顯示省略版的任務資訊
| Name | Required | Description | Default |
|---|---|---|---|
| isId | No | 指定是否為ID查詢模式,默認為否(關鍵字模式) | |
| page | No | 分頁頁碼,默認為第1頁 | |
| query | Yes | 搜尋查詢文字,可以是任務ID或多個關鍵字(空格分隔) | |
| pageSize | No | 每頁顯示的任務數量,默認為5筆,最大20筆 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions 'abbreviated' results but does not define what fields are excluded. No mention of read-only nature, side effects, or auth requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, front-loading the core purpose. However, it could be more structured by adding usage context or output details without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, and the description does not explain the return value format or fields in the 'abbreviated' results. For a search tool with 4 parameters, more detail on pagination and result structure is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no extra meaning beyond the schema for parameters; it only describes the output.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: search tasks by keyword or ID and display abbreviated information. It uses specific verb and resource, and distinguishes from siblings like get_task_detail (full detail) and list_tasks (list all).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by keyword or ID search, but does not explicitly state when not to use or mention alternative tools like get_task_detail for full details. No guidance on when to prefer this over list_tasks or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reflect_taskB
批判性審查分析結果,評估方案完整性並識別優化機會,確保解決方案符合最佳實踐,如果需要提供程式碼請使用 pseudocode 格式且盡量精簡只保留核心實現部分
| Name | Required | Description | Default |
|---|---|---|---|
| summary | Yes | 結構化的任務摘要,保持與分析階段一致以確保連續性 | |
| analysis | Yes | 完整詳盡的技術分析結果,包括所有技術細節、依賴組件和實施方案,如果需要提供程式碼請使用 pseudocode 格式且盡量精簡只保留核心實現部分 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully carries the burden of behavioral disclosure. It fails to state whether the tool is read-only, modifies state, or has any side effects. While it describes the action, it omits crucial behavioral traits like permissions or output characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence that combines purpose and a code format instruction. It is not overly long but lacks structure and could be better organized with bullet points or separate sentences for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not explain the return value or side effects, which is problematic since there is no output schema. It also fails to place the tool within the task workflow, leaving the agent unsure of its role among siblings like 'verify_task'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with detailed descriptions of 'summary' and 'analysis' parameters. The tool description repeats the pseudocode instruction but does not add new meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: critically review analysis results, evaluate completeness, and identify optimization opportunities. It uses specific verbs ('review', 'evaluate', 'identify') and a specific resource ('analysis results'), distinguishing it from sibling tools like 'analyze_task' which likely focuses on initial analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage after analysis but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or exclusions, leaving the agent to infer context from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
split_tasksA
將複雜任務分解為獨立且可追蹤的子任務,建立明確的依賴關係和優先順序。支援四種任務更新模式:追加(append)、覆蓋(overwrite)、選擇性更新(selective)和清除所有任務(clearAllTasks),其中覆蓋模式只會刪除未完成的任務並保留已完成任務,選擇性更新模式可根據任務名稱智能匹配更新現有任務,同時保留其他任務,如果你需要規劃全新的任務請使用清除所有任務模式會清除所有任務並創建備份。請優先使用清除所有任務模式,只有用戶要求變更或修改計畫內容才使用其他模式。
**請參考之前的分析結果提供 pseudocode
**如果任務太多或內容過長,請分批使用「split_tasks」工具,每次只提交一小部分任務
| Name | Required | Description | Default |
|---|---|---|---|
| tasks | Yes | 結構化的任務清單,每個任務應保持原子性且有明確的完成標準 | |
| updateMode | Yes | 任務更新模式選擇:'append'(保留所有現有任務並添加新任務)、'overwrite'(清除所有未完成任務並完全替換,保留已完成任務)、'selective'(智能更新:根據任務名稱匹配更新現有任務,保留不在列表中的任務,推薦用於任務微調)、'clearAllTasks'(清除所有任務並創建備份)。 預設為'clearAllTasks'模式,只有用戶要求變更或修改計劃內容才使用其他模式 | |
| globalAnalysisResult | No | 全局分析結果:來自 reflect_task 的完整分析結果,適用於所有任務的通用部分 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description explains each update mode's behavior (e.g., overwrite deletes only unfinished tasks, clearAllTasks creates a backup). This discloses key behavioral traits, though it could mention error handling or idempotency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose, followed by mode details and usage priority. Some redundancy exists (e.g., repeated mode explanations), but overall it is well-structured and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters, no output schema, and moderate complexity, the description covers all necessary aspects: mode behaviors, usage priority, and batch splitting instructions. It is sufficient for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining the updateMode parameter's usage context (when each mode is appropriate) and the tasks' atomicity requirement, enhancing meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the verb 'split' (分解) and resource 'tasks', and specifies the outcome: creating independent sub-tasks with dependencies and priorities. This distinguishes it from sibling tools like update_task or execute_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly recommends prioritizing the 'clearAllTasks' mode and only using other modes when user requests changes. Also provides guidelines on batching and referencing previous analysis, giving clear when-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_taskB
更新任務內容,包括名稱、描述和注記,但不允許修改已完成的任務
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | 任務的新名稱(選填) | |
| notes | No | 任務的新補充說明(選填) | |
| taskId | Yes | 待更新任務的唯一標識符,必須是系統中存在且未完成的任務ID | |
| description | No | 任務的新描述內容(選填) | |
| dependencies | No | 任務的新依賴關係(選填) | |
| relatedFiles | No | 與任務相關的文件列表,用於記錄與任務相關的代碼文件、參考資料、要建立的檔案等(選填) | |
| implementationGuide | No | 任務的新實現指南(選填) | |
| verificationCriteria | No | 任務的新驗證標準(選填) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds key behavioral info (cannot modify completed tasks) but is incomplete. It does not mention authentication, side effects, or that other schema fields (e.g., dependencies) are actually updatable, potentially misleading the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the action. However, it could be structured to list all updatable fields or reference the schema for completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, no output schema) and the presence of 14 sibling tools, the description is insufficient. It lacks details on return value, error handling (e.g., for completed tasks), and the full scope of updatable fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameter descriptions in the schema are complete. The tool description adds minimal value, only mentioning three fields (name, description, notes) while the schema includes many more, such as dependencies and relatedFiles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool updates task content, listing specific fields (name, description, notes) and includes a constraint (cannot modify completed tasks). However, it does not explicitly distinguish from sibling tools like 'complete_task' or 'update_task_files'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implicit usage context (when to update tasks) and a when-not condition (completed tasks). However, it lacks explicit guidance on when to use this tool versus alternatives like 'update_task_files' or 'complete_task'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_task_filesC
更新任務相關文件列表,用於記錄與任務相關的代碼文件、參考資料等
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | 待更新任務的唯一標識符,必須是系統中存在且未完成的任務ID | |
| relatedFiles | Yes | 與任務相關的文件列表 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It only says 'update', implying mutation, but does not describe whether the update replaces or appends files, whether the task must exist and be incomplete, or any side effects. The schema includes validations, but the description adds no additional context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence in Chinese. It is front-loaded and contains no unnecessary words. However, it could be restructured to include more detail without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has two required parameters and no output schema, the description should provide more context about behavior (e.g., whether the file list is replaced or appended, what happens if taskId is invalid) and return values. The current description is too minimal for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: both 'taskId' and 'relatedFiles' have detailed descriptions in the schema. The tool description does not add new meaning beyond confirming that files are related to the task. With full schema coverage, the baseline is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool updates the task-related file list for recording code files and references. The verb 'update' and resource 'file list' are specific, and the tool is differentiated from sibling tools like 'update_task' which likely updates task metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'update_task' or 'add_file'. There is no mention of prerequisites, context, or conditions that would influence the decision to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
verify_taskB
全面驗證任務完成度,確保所有需求與技術標準都已滿足,並無遺漏細節
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | 待驗證任務的唯一標識符,必須是系統中存在的有效任務ID |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It does not indicate whether the tool is read-only, modifies state, requires special permissions, or what happens upon failure. The phrase 'comprehensively verify' implies a check, but side effects or output behavior are not described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence that immediately conveys the tool's purpose. It is front-loaded with the verb 'verify' and the object 'task completion', with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description should explain what the tool returns or any behavioral details. It does not mention whether it returns a boolean, a detailed report, or throws errors. For a verification tool, this lack of completeness leaves the agent guessing about the outcome.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a single parameter 'taskId' documented as a UUID for a valid task ID. The description adds no further semantic value beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to comprehensively verify task completion, ensuring all requirements and technical standards are met. It uses a specific verb ('verify') and resource ('task'), and distinguishes itself from sibling tools like 'complete_task' (marking done) and 'analyze_task' (analysis).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites or when not to use it. For a verification tool, context on typical usage (e.g., after task execution) would be helpful but is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
15 tool updates
v1.0.0- First observed
analyze_task - First observed
clear_all_tasks - First observed
complete_task - First observed
delete_task - First observed
execute_task - First observed
get_task_detail - First observed
list_tasks - First observed
plan_task - First observed
process_thought - First observed
query_task - First observed
reflect_task - First observed
split_tasks - First observed
update_task - First observed
update_task_files - First observed
verify_task
TDQS
Scored across 15 tools
Most tools have distinct purposes (e.g., delete_task vs complete_task), but analyze_task and reflect_task both involve analysis and could cause confusion. process_thought is a generic cognitive tool that doesn't fit the task management domain. Overall, descriptions help differentiate.
All tools follow a consistent verb_noun pattern (e.g., list_tasks, update_task, complete_task). Even process_thought adheres to this pattern. No mixing of conventions like camelCase or inconsistent verb styles.
With 15 tools, the server is well-scoped for a task manager. Each tool serves a clear role in the task lifecycle, and the count falls within the optimal 3-15 range without feeling overly heavy or thin.
The tool set covers core task management operations: create (plan_task, split_tasks), read (list_tasks, get_task_detail, query_task), update (update_task, update_task_files), delete (delete_task, clear_all_tasks), and completion (complete_task, verify_task). Missing explicit dependency management or prioritization, but analysis tools (analyze, reflect, execute) add depth. process_thought is an outlier.
Maintenance
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- AlicenseBqualityDmaintenanceShrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.1527221MIT
- AlicenseNot gradedqualityDmaintenanceAI-native project management with persistent memory for coding agents. 17 MCP tools for features, stories, sprints, architecture decisions, knowledge base, and session tracking.3MIT
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