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Mallikarjun-Roddannavar

ai-testcase-generator-mcp

πŸ€– 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 metadataβ€”powered 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

✨ Features

  • πŸ”Œ MCP-compliant server (stdio transport).

  • πŸ“ Tool: generate_tests_excel

    • Input: 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: πŸ“Š Excel test plan with columns: Sl no, Test Name, Pre-Condition, Steps, Expected Result.

  • 🧠 Prompt-driven test generation with configurable LLM (Groq, OpenAI, Anthropic).

  • πŸ“œ Detailed logging with Winston.

Related MCP server: Swagger Testcase MCP

πŸ—οΈ 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]

πŸ“‚ Project Structure

ai-testcase-designer-mcp/
β”œβ”€β”€ build/                         # Compiled JavaScript output
β”œβ”€β”€ assets/                        # Demo gifs, images, and sample files
β”‚    β”œβ”€β”€ demo.gif
β”‚    β”œβ”€β”€ excel_preview.png
β”‚    └── sample_chat_message.txt
β”œβ”€β”€ configs/
β”‚    └── config.json               # Server/tool config
β”œβ”€β”€ src/
β”‚    β”œβ”€β”€ index.ts                  # Main server entry point (MCP interface & routing)
β”‚    β”œβ”€β”€ excel.ts                  # Excel file creation & writing logic (modular)
β”‚    β”œβ”€β”€ logger.ts                 # Winston logger configuration & log writing (modular)
β”‚    └── prompts/
β”‚         └── testcase_prompt.txt  # Prompt template for LLM-based test generation
β”œβ”€β”€ package.json
β”œβ”€β”€ tsconfig.json
β”œβ”€β”€ README.md
└── .gitignore
  • src/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.


πŸŽ₯ Demo

Here’s the MCP generating test cases and exporting to Excel:

AI Testcase Designer Demo

πŸ” Excel Preview

Below is a quick preview of the generated test cases:

Excel Preview

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

βš™οΈ Installation

Follow these steps to set up the AI Testcase Designer MCP server locally:

  1. Clone the repository

    git clone https://github.com/yourusername/ai-testcase-designer-mcp.git
    cd ai-testcase-designer-mcp
  2. Install dependencies

    npm install
  3. Build the server

    npm run build
  4. Configure 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.json

      • On 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:

    1. Install Cline from the VS Code Marketplace.

    2. Open the Cline sidebar (from the VS Code activity bar).

    3. Go to the "MCP Servers" section and click "Add New MCP Server".

    4. 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"
          }
        }
      }
    5. Test the connection and save.

    For a visual step-by-step guide, see below:

    Cline MCP Server Add Steps

    Cline MCP Server Connection Success

    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.yaml under mcp_servers:

mcp_servers:
  ai-testcase-designer-mcp:
    command: "node"
    args: ["/absolute/path/to/ai-testcase-designer-mcp/build/index.js"]

πŸ”‘ API Key & Work Directory Setup

To use the AI Testcase Designer MCP.

  1. Get your Groq API key from here for free: https://console.groq.com/keys

  2. 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

  1. πŸ–₯️ Open Claude Desktop (or any MCP-compatible client).

  2. πŸ“‚ Download Sample Chat Message: sample_chat_message.txt and copy its content.

  3. βœ‰οΈ Paste the content into the chat and send the message: the AI will generate detailed test cases in Excel format.

  4. πŸ’Ύ Generated Excel files and server logs are saved in your WORK_DIR folder.

▢️ 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."
  }
}

πŸ“Š 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

πŸ“‚ 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 inspector

The 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 tool
generate_tests_excelA

Generate a comprehensive API test plan with positive, negative, and boundary value analysis test cases. Output is Excel with columns: Sl no, Test Name, Pre-Condition, Steps, Expected Result

ParametersJSON Schema
NameRequiredDescriptionDefault
endpointYesAPI endpoint to test (e.g., https://api.example.com/v1/users)
methodYesHTTP method (GET, POST, etc.)
payloadNoSample payload for the request (if applicable)
extraContextNoAny additional instructions/context for the test plan

TDQS

A3.7/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral transparency. It fails to disclose whether authentication is needed, if the endpoint must be live, potential rate limits, or how the payload is used. The word 'comprehensive' is vague, and there is no mention of what happens with large outputs or errors.

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?

Two sentences: first covers purpose and test types, second describes output format. No superfluous content. Every sentence adds value. Ideal conciseness for a relatively straightforward tool.

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

Completeness3/5

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

Given the moderate complexity (generating test plans) and lack of output schema, the description should explain the output more (e.g., is it a file download?) and mention potential time or size constraints. It partially covers the output with column names but omits behavioral context. Adequate but with gaps.

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?

Input schema coverage is 100% with clear descriptions for all four parameters, so baseline is 3. The tool description does not add any parameter-specific detail beyond what the schema already provides, so it neither improves nor detracts from the schema's clarity.

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 it generates a comprehensive API test plan with positive, negative, and boundary value analysis, and specifies the Excel output columns. It uses a specific verb ('Generate') and resource ('API test plan'), making the purpose unambiguous, especially since there are no sibling tools to differentiate from.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context about what the tool does and its output, but does not explicitly state when to use it or when not to. Since there are no sibling tools, the lack of exclusion criteria is less critical, but some guidance on prerequisites (e.g., valid endpoint) would improve it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

TDQS

A3.6/5.0
Disambiguation5/5

Only one tool exists, so there is no risk of confusion with other tools.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern (generate_tests_excel), which is consistent.

Tool Count2/5

A single tool for a test case generator seems too few; typically one would expect multiple tools for different operations (e.g., generating, configuring, listing templates) or varying inputs.

Completeness2/5

The tool covers only generating a test plan in Excel, lacking other common operations such as generating for specific APIs, customizing output, or handling different input formats.

Maintenance

ActivityNo data
ResponsivenessSyncing

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