ai-testcase-designer-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ai-testcase-designer-mcpGenerate test cases for POST /api/users with sample payload and export to Excel."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
π€ AI Testcase Generator MCP
An Model Context Protocol(MCP) server that generates comprehensive API test plans (positive, negative, and boundary/edge cases) directly from endpoint metadataβpowered by LLMs.
This is a TypeScript-based Model Context Protocol(MCP) server for QA engineers. It demonstrates core Model Context Protocol concepts by providing:
AI-powered tool for generating exhaustive test case plans from API endpoints and payloads
Prompt-driven LLM integration for quality and coverage
Extensible structure for future automation tooling
β¨ Features
π MCP-compliant server (
stdiotransport).π Tool:
generate_tests_excelInput: endpoint, HTTP method, payload, extra context.
Input options:
Direct endpoint details: endpoint, HTTP method, payload
Use extraContext to provide any additional testing notes or constraints
OutputPut: π Excel test plan with columns: Sl no, Test Name, Pre-Condition, Steps, Expected Result.
π§ Prompt-driven test generation with configurable LLM (Groq, OpenAI, Anthropic).
π Detailed logging with Winston.
Related MCP server: ai-testcase-generator-mcp
ποΈ Architecture
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]π Project Structure
ai-testcase-designer-mcp/
βββ build/ # Compiled JavaScript output
βββ assets/ # Demo gifs, images, and sample files
β βββ demo.gif
β βββ excel_preview.png
β βββ sample_chat_message.txt
βββ configs/
β βββ config.json # Server/tool config
βββ src/
β βββ index.ts # Main server entry point (MCP interface & routing)
β βββ excel.ts # Excel file creation & writing logic (modular)
β βββ logger.ts # Winston logger configuration & log writing (modular)
β βββ prompts/
β βββ testcase_prompt.txt # Prompt template for LLM-based test generation
βββ package.json
βββ tsconfig.json
βββ README.md
βββ .gitignoresrc/excel.ts: Handles all Excel (.xlsx) file creation and test plan export (modularized).
src/logger.ts: Provides modular logging functionality across the MCP server using Winston.
src/prompts/: Contains prompt templates for LLM-driven test generation.
assets/: Demo GIFs, Excel sample preview, and chat prompt examples.
π₯ Demo
Hereβs the MCP generating test cases and exporting to Excel:

π Excel Preview
Below is a quick preview of the generated test cases:

Development
Install dependencies:
npm installBuild the server:
npm run buildFor development with auto-rebuild:
npm run watchβοΈ Installation
Follow these steps to set up the AI Testcase Designer MCP server locally:
Clone the repository
git clone cd ai-testcase-designer-mcpInstall dependencies
npm installBuild the server
npm run buildConfigure the server in your MCP client
a. Claude Desktop or any MCP-compatible client
Add the following server configuration:
On MacOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonOn Windows:
%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 Extension)
Quick Start:
Install Cline from the VS Code Marketplace.
Open the Cline sidebar (from the VS Code activity bar).
Go to the "MCP Servers" section and click "Add New MCP Server".
Fill in the server details:
{ "mcpServers": { "ai-testcase-designer-mcp": { "disabled": false, "timeout": 60, "command": "node", "args": [ "c:/Auto_WS/ai-testcase-designer-mcp/build/index.js" ], "transportType": "stdio" } } }Test the connection and save.
For a visual step-by-step guide, see below:


For detailed Cline guidance, see the official docs:
cline.bot/getting-started/installing-cline#vs-code-marketplace%3A-step-by-step-setup
c. Hermes Agent
Add the following server configuration to
~/.hermes/config.yamlundermcp_servers:
mcp_servers:
ai-testcase-designer-mcp:
command: "node"
args: ["/absolute/path/to/ai-testcase-designer-mcp/build/index.js"]π API Key & Work Directory Setup
To use the AI Testcase Designer MCP.
Get your Groq API key from here for free: https://console.groq.com/keys
A working directory (WORK_DIR) where generated Excel test plans and server logs will be saved.
Update your config.json file like this:
{
"MODEL_API_KEY": "gsk_7Ma3Fabcd <your-api-key-here>",
"WORK_DIR": "C:/Auto_WS/ai-testcase-designer-mcp"
}How to Use
π₯οΈ Open Claude Desktop (or any MCP-compatible client).
π Download Sample Chat Message: sample_chat_message.txt and copy its content.
βοΈ Paste the content into the chat and send the message: the AI will generate detailed test cases in Excel format.
πΎ Generated Excel files and server logs are saved in your
WORK_DIRfolder.
βΆοΈ Example Request
{
"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."
}
}π Example Excel Output
Sl no | Test Name | Pre-Condition | Steps | Expected Result |
1 | Valid User Create | DB is empty | Send POST with valid payload | User created successfully |
2 | Missing Email | DB is empty | Send POST with name only | 400 validation error |
3 | Invalid Email | DB is empty | Send POST with invalid email format | 422 error message |
π Files Output
Files are written to: ./workdir/generated/
Sample Log Output
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)Debugging
npm run inspectorThe Inspector will provide a URL to access debugging tools in your browser.
License
This project is licensed under the MIT License.
See the LICENSE file for details.
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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