ai-testcase-designer-mcp
🤖 AI 테스트케이스 생성기 MCP
엔드포인트 메타데이터에서 직접 포괄적인 API 테스트 계획(긍정, 부정, 경계/엣지 케이스)을 생성하는 Model Context Protocol(MCP) 서버입니다. LLM 기반으로 동작합니다.
이것은 QA 엔지니어를 위한 TypeScript 기반 Model Context Protocol(MCP) 서버입니다. 핵심 Model Context Protocol 개념을 다음과 같이 보여줍니다:
API 엔드포인트와 페이로드에서 철저한 테스트 케이스 계획을 생성하는 AI 기반 도구
품질과 커버리지를 위한 프롬프트 기반 LLM 통합
향후 자동화 도구를 위한 확장 가능한 구조
✨ 기능
🔌 MCP 호환 서버 (
stdio전송).📝 도구:
generate_tests_excel입력: 엔드포인트, HTTP 메서드, 페이로드, 추가 컨텍스트.
입력 옵션:
직접 엔드포인트 세부 정보: 엔드포인트, HTTP 메서드, 페이로드
추가 테스트 노트나 제약 조건을 제공하려면 extraContext를 사용하세요.
출력: 📊 Excel 테스트 계획 — 열: Sl no, Test Name, Pre-Condition, Steps, Expected Result.
🧠 프롬프트 기반 테스트 생성 — 구성 가능한 LLM(Groq, OpenAI, Anthropic).
📜 Winston을 사용한 상세 로깅.
Related MCP server: ai-testcase-generator-mcp
🏗️ 아키텍처
flowchart TD
A[Claude / MCP Client] -->|Run Tool| B[MCP Server]
B -->|Prompt| C[LLM API]
C -->|Test Cases JSON| B
B -->|Excel Export| D[(Test Plan .xlsx)]
B -->|Logs| E[Server Log File]📂 프로젝트 구조
ai-testcase-designer-mcp/
├── build/ # Compiled JavaScript output
├── assets/ # Demo gifs, images, and sample files
│ ├── demo.gif
│ ├── excel_preview.png
│ └── sample_chat_message.txt
├── configs/
│ └── config.json # Server/tool config
├── src/
│ ├── index.ts # Main server entry point (MCP interface & routing)
│ ├── excel.ts # Excel file creation & writing logic (modular)
│ ├── logger.ts # Winston logger configuration & log writing (modular)
│ └── prompts/
│ └── testcase_prompt.txt # Prompt template for LLM-based test generation
├── package.json
├── tsconfig.json
├── README.md
└── .gitignoresrc/excel.ts: 모든 Excel(.xlsx) 파일 생성 및 테스트 계획 내보내기를 처리합니다(모듈화됨).
src/logger.ts: Winston을 사용하여 MCP 서버 전반에 걸친 모듈식 로깅 기능을 제공합니다.
src/prompts/: LLM 기반 테스트 생성을 위한 프롬프트 템플릿을 포함합니다.
assets/: 데모 GIF, Excel 샘플 미리보기, 채팅 프롬프트 예제를 포함합니다.
🎥 데모
MCP가 테스트 케이스를 생성하고 Excel로 내보내는 모습입니다:

🔍 Excel 미리보기
생성된 테스트 케이스의 빠른 미리보기입니다:

개발
의존성 설치:
npm install서버 빌드:
npm run build자동 재빌드가 포함된 개발용:
npm run watch⚙️ 설치
AI 테스트케이스 디자이너 MCP 서버를 로컬에 설정하려면 다음 단계를 따르세요:
저장소 클론
git clone cd ai-testcase-designer-mcp의존성 설치
npm install서버 빌드
npm run buildMCP 클라이언트에서 서버 구성
a. Claude Desktop 또는 MCP 호환 클라이언트
다음 서버 구성을 추가하세요:
MacOS에서:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows에서:
%APPDATA%/Claude/claude_desktop_config.json
{ "mcpServers": { "ai-testcase-designer-mcp": { "disabled": false, "timeout": 60, "command": "node", "args": [ "c:/Auto_WS/ai-testcase-designer-mcp/build/index.js" ], "transportType": "stdio" } } }b. Cline (VS Code 확장 프로그램)
빠른 시작:
Cline 사이드바를 엽니다 (VS Code 활동 막대에서).
"MCP Servers" 섹션으로 이동하여 "Add New MCP Server"를 클릭합니다.
서버 세부 정보를 입력합니다:
{ "mcpServers": { "ai-testcase-designer-mcp": { "disabled": false, "timeout": 60, "command": "node", "args": [ "c:/Auto_WS/ai-testcase-designer-mcp/build/index.js" ], "transportType": "stdio" } } }연결을 테스트하고 저장합니다.
시각적 단계별 가이드는 아래를 참조하세요:


자세한 Cline 안내는 공식 문서를 참조하세요:
cline.bot/getting-started/installing-cline#vs-code-marketplace%3A-step-by-step-setup
c. Hermes Agent
~/.hermes/config.yaml의mcp_servers아래에 다음 서버 구성을 추가하세요:
mcp_servers:
ai-testcase-designer-mcp:
command: "node"
args: ["/absolute/path/to/ai-testcase-designer-mcp/build/index.js"]🔑 API 키 및 작업 디렉토리 설정
AI 테스트케이스 디자이너 MCP를 사용하려면:
여기에서 무료로 Groq API 키를 받으세요: https://console.groq.com/keys
생성된 Excel 테스트 계획과 서버 로그가 저장될 작업 디렉토리(WORK_DIR)를 준비하세요.
config.json 파일을 다음과 같이 업데이트하세요:
{
"MODEL_API_KEY": "gsk_7Ma3Fabcd <your-api-key-here>",
"WORK_DIR": "C:/Auto_WS/ai-testcase-designer-mcp"
}사용 방법
🖥️ Claude Desktop(또는 MCP 호환 클라이언트)을 엽니다.
📂 샘플 채팅 메시지 다운로드: sample_chat_message.txt를 다운로드하고 내용을 복사합니다.
✉️ 내용을 채팅에 붙여넣고 메시지를 보냅니다. AI가 Excel 형식의 상세 테스트 케이스를 생성합니다.
💾 생성된 Excel 파일과 서버 로그는
WORK_DIR폴더에 저장됩니다.
▶️ 예제 요청
{
"name": "generate_tests_excel",
"arguments": {
"endpoint": "https://api.example.com/v1/users",
"method": "POST",
"payload": {
"name": "John Doe",
"email": "john@example.com"
},
"extraContext": "Focus on invalid email and empty payload scenarios."
}
}📊 예제 Excel 출력
일련번호 | 테스트 이름 | 사전 조건 | 단계 | 예상 결과 |
1 | 유효한 사용자 생성 | DB가 비어 있음 | 유효한 페이로드로 POST 전송 | 사용자가 성공적으로 생성됨 |
2 | 이메일 누락 | DB가 비어 있음 | 이름만으로 POST 전송 | 400 검증 오류 |
3 | 잘못된 이메일 | DB가 비어 있음 | 잘못된 이메일 형식으로 POST 전송 | 422 오류 메시지 |
📂 파일 출력
파일은 ./workdir/generated/에 저장됩니다.
샘플 로그 출력
2025-09-13T10:22:11 [info]: [Step1] Incoming request: endpoint=/v1/users, method=POST
2025-09-13T10:22:11 [info]: [Step2] Building LLM prompt...
2025-09-13T10:22:13 [info]: [Step5] Converting LLM JSON to Excel rows (15 test cases)디버깅
npm run inspectorInspector는 브라우저에서 디버깅 도구에 접근할 수 있는 URL을 제공합니다.
라이선스
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다.
자세한 내용은 LICENSE 파일을 참조하세요.
Available Tools
1 toolgenerate_tests_excelB
Generate a comprehensive API test plan with positive, negative, and boundary value analysis test cases and export to Excel (.xlsx). You (Cursor IDE model) can generate test cases directly into the testCases array argument, or pass endpoint, method, and payload to generate via external LLM if configured.
| Name | Required | Description | Default |
|---|---|---|---|
| method | Yes | HTTP method (GET, POST, PUT, DELETE, etc.) | |
| payload | No | Sample payload for the request (if applicable) | |
| endpoint | Yes | API endpoint to test (e.g., https://api.example.com/v1/users) | |
| testCases | No | Direct list of test cases generated by Cursor IDE's AI model. Bypasses external LLM API if provided. | |
| extraContext | No | Any additional instructions/context for the test plan |
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 of behavioral disclosure. It does disclose the core behavior: generating test cases and exporting to Excel, and it mentions the bypass mechanism for external LLM. However, it does not disclose potential side effects (e.g., file creation, network calls to external LLM, or failure modes if no external LLM is configured). The description is partially transparent but leaves out important behavioral details such as whether the tool makes external API calls when testCases are not provided, and what happens if the external LLM is not configured.
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 moderately concise, fitting in two sentences. It front-loads the core purpose (generating test plan and exporting to Excel) and then explains the two modes of use. The structure is effective, but the second sentence contains a parenthetical that adds some complexity, though it's still efficient. No wasted words, but it could be slightly more streamlined.
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 this tool (5 parameters, nested objects, two modes of operation, no output schema), the description provides a decent overview but misses several critical details. It does not explain what happens when both testCases and endpoint/method are provided (which takes precedence), it doesn't mention the format of the Excel file or where it is saved, and it doesn't clarify what is required for the external LLM to be used. The absence of an output schema means the description should clarify what the tool returns, but it only says 'export to Excel' without specifying the output format. This is a significant gap for a tool that likely produces a file artifact.
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 has 100% description coverage, meaning that every parameter has a description in the schema itself. The description adds extra context about the testCases parameter, explaining that it bypasses the external LLM, and explains the alternative use of endpoint/method/payload. This adds value beyond the schema but is not exhaustive for all parameters. With full schema coverage, the baseline is 3, and the description slightly enhances clarity for the testCases parameter, but doesn't add semantic depth to method or endpoint beyond what the schema provides.
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: generating a comprehensive API test plan with positive, negative, and boundary value analysis, and exporting to Excel. It distinguishes itself by mentioning two modes of operation (direct test case generation or via external LLM), which is a specific feature. However, it does not compare to any sibling tools since none are provided, so it lacks explicit differentiation in that regard.
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 when to use this tool (when generating test plans for APIs) and provides context on how to use it (either provide testCases directly or provide endpoint/method/payload for external generation). However, it does not specify when not to use it or mention alternatives, leaving some ambiguity about the exact selection criteria. The guidance is present but not explicit about exclusions.
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.
1 tool update
v0.1.0- First observed
generate_tests_excel
TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion between tools. The single tool's purpose is clearly defined.
With only one tool, naming consistency is trivially satisfied. The name 'generate_tests_excel' follows a clear verb_noun pattern and accurately describes the action.
Having just one tool feels thin for a server named 'ai-testcase-designer-mcp', which suggests a broader scope. The single tool does combine generation and export, but it may leave agents wanting for more modular capabilities.
The tool covers the core workflow of generating and exporting test cases to Excel, with support for both manual input and external LLM generation. However, it lacks separate operations like previewing, editing, or managing test case files, which are minor gaps given the narrow purpose.
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