QA Copilot AI
MCP server for AI-powered QA analysis. It enables analyzing test failures, identifying root causes, suggesting fixes, classifying defects, detecting flaky tests, and generating test cases and bug reports.
README
š¤ QA Copilot AI

An AI-powered QA Assistant that helps QA engineers analyze test failures, identify root causes, generate fixes, classify defects, detect flaky tests, and automate testing activities using MCP, TypeScript, and Large Language Models.
š Project Overview
Modern QA teams spend significant time analyzing failed automation tests, debugging errors, writing bug reports, and identifying flaky tests.
QA Copilot AI is an AI-assisted testing platform designed to reduce debugging effort by combining:
- Model Context Protocol (MCP)
- Large Language Models (Groq LLM)
- Playwright automation data
- REST APIs
- TypeScript backend services
The system allows QA engineers to provide test reports and receive AI-generated insights such as:
- Root cause analysis
- Failure classification
- Suggested fixes
- Test improvement recommendations
- Automated QA artifacts
šÆ Problem Statement
Automation failures often require manual investigation:
ā Reading long stack traces ā Finding root causes ā Identifying flaky tests ā Writing defect reports ā Creating repetitive test cases
QA Copilot AI solves this by acting as an intelligent testing assistant.
⨠Features
AI-Powered QA Analysis
ā Failure Analysis ā Test Report Analysis ā Root Cause Identification ā Fix Suggestions ā Failure Classification ā Flaky Test Detection
Test Automation Assistance
ā API Test Generator ā Test Case Generator ā Playwright Script Generator ā Bug Report Generator
Engineering Features
ā MCP Server Implementation ā REST API using Express.js ā Modular TypeScript Architecture ā Environment Configuration ā Error Handling ā Logging Utilities
š System Architecture

Workflow:
QA Engineer
|
|
Test Reports / Logs
|
v
QA Copilot API
|
v
MCP Server
|
|
-----------------------------
| | | |
Failure Report Fix Flaky
Analyzer Analyzer Suggest Detector
|
v
Groq Large Language Model
|
v
AI Generated QA Insights
š Tech Stack
| Category | Technology |
|---|---|
| Language | TypeScript |
| Runtime | Node.js |
| Backend | Express.js |
| AI Model | Groq LLM |
| Protocol | Model Context Protocol |
| Automation | Playwright |
| Package Manager | npm |
| Configuration | dotenv |
š Project Structure
QA-Copilot-AI
src/
āāā ai/
ā āāā promptTemplates.ts
|
āāā api/
|
āāā mcp-server/
|
āāā tools/
|
āāā services/
|
āāā config/
|
āāā utils/
docs/
āāā architecture.png
āāā architecture.md
screenshots/
package.json
README.md
ā Installation
Clone repository:
git clone https://github.com/Sham-coder16/qa-copilot-ai.git
Navigate:
cd qa-copilot-ai
Install dependencies:
npm install
š Environment Setup
Create .env
GROQ_API_KEY=your_api_key
PORT=3000
ā¶ Running Application
Start MCP Server
npm run mcp
Expected:
MCP SERVER STARTED
Tools Registered Successfully
Start API Server
npm run api
Expected:
API Server running on port 3000
Run Test Client
npx tsx src/test-client.ts
š§ MCP Tools
Available tools:
| Tool | Purpose |
|---|---|
| Failure Analyzer | Analyze failed tests |
| Report Analyzer | Analyze execution reports |
| Suggest Fix | Generate solutions |
| Failure Classification | Categorize failures |
| Flaky Detector | Detect unstable tests |
| Test Generator | Generate automation tests |
| Bug Generator | Create defect reports |
š” REST API Example
Endpoint:
POST /analyze
Request:
{
"failure": "Login test failed due to timeout"
}
Response:
{
"category":"Automation Failure",
"rootCause":"Element loading issue",
"suggestion":"Increase explicit wait"
}
šø Screenshots
šø Project Screenshots
MCP Server Running

API Server Running

AI Failure Analysis

Bug Report Generation

Test Case Generation

š Future Enhancements
- Docker Support
- GitHub Actions CI/CD
- Jenkins Pipeline Integration
- Database Storage
- Web Dashboard
- Multiple LLM Provider Support
- Playwright HTML Report Integration
š” Skills Demonstrated
- QA Automation
- Playwright Testing
- TypeScript Development
- Node.js Backend
- REST API Development
- AI Integration
- Prompt Engineering
- MCP Architecture
- LLM Testing Concepts
- GitHub Project Management
š©āš» Author
Shamli Kadukar
GitHub:
https://github.com/Sham-coder16
ā If this project helped you understand AI-powered QA automation, consider starring the repository.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.