igel-qe-core

IGEL QE Developer Experience Layer (CLI & Model Context Protocol Server)

igel-qe-core provides the CLI and Model Context Protocol (MCP) server integration for the IGEL Quality Engineering (QE) AI Platform. It enables AI-driven test case design, automated test script generation (pytest & Page Object Models), code impact analysis, requirement context enrichment, and direct integration into AI-assisted IDEs such as VS Code (GitHub Copilot, Cline), Cursor, and Windsurf.

🌟 Key Features

• 🛠 Developer CLI (igel-qe): Easily initialize projects, generate test suites from Jira keys, run code impact analysis, and manage IDE configurations.
• 🔌 Native MCP Server (igel-qe-mcp): Connect your IDE's AI agents directly to the IGEL QE agentic engine with rich Model Context Protocol tools.
• 🎯 Automated Test Design & Generation: Extract requirements and business rules from Jira issues and construct structured test scenarios and approved test cases.
• ⚙️ Automation Code Generation: Design and generate robust pytest automation scripts reusing Page Objects and test utilities.
• 🔍 Impact Analysis & Delta Sync: Track code changes from commits or files to determine regression risk across requirements and test suites.
• 🔄 Continuous Feedback Loop: Submit granular feedback at requirement, scenario, test case, or step levels to refine generation accuracy over time.
• 📂 Context Management: Scaffolds and enriches framework context.md files with Python AST parsing and dependency graph extraction.

📦 Installation

Global Installation via npm

npm install -g igel-qe-core

Or execute directly via npx:

npx igel-qe-core init

🚀 Quick Start

1. Initialize IGEL QE in Your Workspace

Run the init command inside your project directory to set up virtual environments, Python dependencies, agent prompt templates, and MCP configuration.

# Basic initialization
igel-qe init

# Auto-configure for GitHub Copilot in VS Code
igel-qe init --with-copilot

# Auto-configure for Cline
igel-qe init --with-cline

# Auto-configure all supported platforms
igel-qe init --with-all

2. Check Project Status

Verify system readiness, asset counts, requirement counts, and connector sync state:

igel-qe status

For JSON output:

igel-qe status --json

3. Generate Test Cases from a Jira Requirement

Generate manual test scenarios and test cases directly from a Jira issue key:

igel-qe generate --jira-key IGEL-1234

Optionally scope under a specific sub-requirement ID:

igel-qe generate --jira-key IGEL-1234 --test-reqid REQ-01

4. Perform Impact Analysis

Analyze the regression impact of changed source files or a specific git/Bitbucket commit:

# Impact analysis on specific files
igel-qe impact --files src/services/auth.py src/models/user.py

# Impact analysis from a commit hash
igel-qe impact --commit a1b2c3d4

5. Submit Corrective Feedback

Provide feedback to improve future AI test generations:

igel-qe feedback \
  --level testcase \
  --target TC_IGEL-1234_S01_P \
  --type correction \
  --comment "Step 3 should include multi-factor authentication prompt validation."

💻 Model Context Protocol (MCP) Integration

The included igel-qe-mcp executable provides a standard Model Context Protocol (stdio) server allowing AI agents inside VS Code, Cursor, Windsurf, or Claude Desktop to interact directly with IGEL QE workflows.

Adding MCP Server to Configuration

Add igel-qe-mcp to your IDE's MCP config file (e.g. .mcp.json or VS Code settings):

{
  "mcpServers": {
    "igel-qe": {
      "command": "igel-qe-mcp",
      "args": []
    }
  }
}

Or configure via CLI:

igel-qe platform setup copilot

Available MCP Tools

| MCP Tool | Description | |---|---| | plan_test_cases | Research Jira requirement and generate a test design plan. | | generate_test_cases | Generate structured test scenarios and manual test cases from Jira. | | plan_automation | Design reusable automation plan (page objects, fixtures, utilities). | | generate_automation | Generate pytest automation scripts for approved test cases. | | impact_analysis | Run regression impact analysis for modified source files. | | sync_bitbucket | Delta sync Python asset changes from a Bitbucket commit hash. | | sync_from_files | Delta sync from an explicit list of changed file paths. | | analyze_framework_scope | Analyze changed files and update impacted framework assets. | | enrich_requirement_context | Update requirement context and sync database mappings. | | enrich_framework_context | Enrich context.md files using Python AST & dependency graphs. | | update_context_file | Regenerate AST/dependency-aware content for a single context file. | | submit_feedback | Submit corrective feedback to refine agent intelligence. | | get_project_status | Return project state summary, connector status, and readiness. | | init_project | Scaffold .ai/ directory and execute pre-flight checks. |

⚙️ Environment Configuration

IGEL QE connects to Azure OpenAI, PostgreSQL, Jira/Xray, and Bitbucket. Ensure the following environment variables are set in your environment or .env file:

Azure OpenAI Configuration

AZURE_OPENAI_API_KEY=your_azure_openai_key
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT=gpt-4o
AZURE_OPENAI_API_VERSION=2024-02-15-preview

Database Configuration (PostgreSQL)

DB_HOST=127.0.0.1
DB_PORT=5432
DB_NAME=igel_qe
DB_USER=postgres
DB_PASSWORD=your_password

Integrations & Connectors (Optional)

JIRA_URL=https://your-domain.atlassian.net
JIRA_API_TOKEN=your_jira_token
[email protected]
BITBUCKET_TOKEN=your_bitbucket_token

🏗 Development & Building

To build the TypeScript source code locally:

npm run build

This compiles TypeScript files from src/ to dist/ (dist/cli/index.js and dist/mcp/server.js).

📄 License

MIT © IGEL Technology