QA Automation MCP Server
An AI-powered QA automation tool that crawls web pages, generates Playwright-based pytest tests using an LLM, runs them, and automatically heals failing tests.
README
QA Automation MCP Server
An AI-powered QA automation tool built on the Model Context Protocol (MCP). It crawls web pages, generates Playwright-based pytest tests using an LLM, runs them, and automatically heals failing tests — all driven by natural language instructions.
How It Works
User Instruction
│
▼
crawl_page(url)
│ Headless Chromium extracts all interactive elements
▼
write_test(crawl_data, instruction, output_dir)
│ Groq LLM generates a pytest + Playwright test file
▼
run_test(crawl_data, output_dir)
│ Pytest executes the generated test
│
├── PASS → HTML report + text summary
│
└── FAIL (locator/timeout error)
│
▼
Auto-Healer
│ Groq rewrites the broken locators
▼
Re-run test
│
└── HTML report + text summary (marked "healed")
Features
- Page Crawling — Extracts inputs, buttons, and links with CSS selectors, XPaths, and metadata
- AI Test Generation — Converts a plain-English instruction + crawl data into a complete pytest test file
- Automated Execution — Runs tests programmatically with
pytestand captures all output - Auto-Healing — Detects locator/timeout failures and sends the broken test back to the LLM for repair, then re-runs automatically
- Rich Reporting — Generates an HTML report with screenshots and a text summary; opens in browser automatically
- Headless or Headed — Browser is headless by default; pass
show_browser=trueto watch it run
Architecture
MCP-server/
├── main.py # MCP server entry point and request dispatcher
├── pyproject.toml # Project metadata and dependencies
├── .env # API keys (not committed)
└── src/
├── tools.py # MCP tool definitions (schemas exposed to the client)
├── handlers.py # Request handlers that wire tools to logic
├── crawler.py # Playwright-based page crawling
├── writer.py # LLM-powered test code generation
├── runner.py # Pytest execution and result parsing
├── healer.py # Auto-healing logic for failing tests
└── reporter.py # HTML and text report generation
MCP Tools
The server exposes three tools to any MCP-compatible client.
crawl_page
Crawls a URL and returns structured data about all interactive elements on the page.
| Parameter | Type | Description |
|---|---|---|
url |
string | The page URL to crawl |
Returns: JSON with title, inputs, buttons, and links, each containing locator strategies (CSS selector, XPath) and metadata.
write_test
Generates a pytest + Playwright test file from crawl data and a plain-English instruction.
| Parameter | Type | Description |
|---|---|---|
crawl_data |
string | JSON output from crawl_page |
instruction |
string | What to test, e.g. "submit the login form" |
output_dir |
string | Root directory where test files will be saved |
show_browser |
boolean | Show the browser during execution (default: false) |
Returns: Path to the generated test file at {output_dir}/generated_tests/test_*.py.
run_test
Executes the generated test and returns a report. Automatically attempts to heal locator/timeout failures.
| Parameter | Type | Description |
|---|---|---|
crawl_data |
string | JSON from crawl_page (used for healing) |
output_dir |
string | Project root directory |
test_file |
string | Path to test file (optional; defaults to last generated) |
Returns: Text summary + path to the HTML report at {output_dir}/test_results/report_*.html.
Prerequisites
- Python 3.12+
- uv (recommended) or pip
- A Groq API key
- Playwright browsers installed
Installation
1. Clone the repository
git clone <repo-url>
cd MCP-server
2. Install dependencies
uv sync
Or with pip:
pip install -e .
3. Install Playwright browsers
uv run playwright install chromium
4. Create a .env file
GROQ_API_KEY=your_groq_api_key_here
Running the Server
The server communicates over stdio, which is the standard transport for MCP clients like Claude Code.
uv run python main.py
Connecting to Claude Code
Add the server to your Claude Code MCP configuration (.claude/settings.json or ~/.claude/settings.json):
{
"mcpServers": {
"qa-automation": {
"command": "uv",
"args": ["run", "python", "main.py"],
"cwd": "/absolute/path/to/MCP-server"
}
}
}
Once connected, you can drive the entire QA workflow through natural language in Claude Code:
"Crawl https://example.com, then write a test that fills in the search box and clicks Submit, and run it."
Generated Output
Each test run produces the following structure inside output_dir:
{output_dir}/
├── generated_tests/
│ └── test_<slug>.py # Generated pytest test file
├── test_results/
│ └── report_YYYYMMDD_HHMMSS.html # HTML report with screenshot
└── result.png # Screenshot from the last test run
The HTML report is opened automatically in your default browser after each run.
Auto-Healing
When a test fails due to a locator or timeout error, the healer:
- Parses the pytest output to identify the broken locator
- Sends the original test code, the error, and the fresh crawl data to the LLM
- Receives a rewritten version with corrected selectors (
get_by_role,get_by_text, or CSS) - Saves the patched file and re-runs the test
- Marks the final report with
healed: true
Only one healing attempt is made per run. Logic errors (wrong assertions, incorrect flow) are not healed automatically.
Dependencies
| Package | Purpose |
|---|---|
mcp / fastmcp |
MCP server framework |
playwright |
Browser automation |
pytest |
Test execution |
pytest-playwright |
Playwright fixtures for pytest |
groq |
LLM API for test generation/healing |
python-dotenv |
.env file loading |
httpx |
Async HTTP client |
pydantic |
Data validation |
LLM Configuration
- Provider: Groq
- Model:
llama-3.3-70b-versatile - Temperature:
0.3for test generation,0.1for healing (tighter, more deterministic)
To switch models or providers, update src/writer.py and src/healer.py.
Limitations
- Auto-healing only addresses locator and timeout errors, not test logic errors
- Only one healing attempt per test run
- Authentication and multi-step login flows are not handled automatically
- Hardcoded to Groq's
llama-3.3-70b-versatilemodel
License
MIT
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
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.