Which integrations are available for this server?

Targets Android emulators and real devices for native mobile test execution. Runs native mobile UI tests on iOS and Android devices using Appium. Runs web end-to-end tests on the Firefox browser via Playwright. Executes API tests against GraphQL endpoints, including query resolution and assertions. Targets iOS simulators and real devices for native mobile test execution. Fetches user stories and acceptance criteria from Jira for automated test generation. Runs end-to-end billing tests for Stripe subscriptions, payments, refunds, and webhook security validation. Executes mobile automation tests using WebdriverIO in conjunction with Appium.

How do I use AI Test Automation MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AI Test Automation MCP Server Generate Playwright tests for Jira story ABC-123" 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.

AI Test Automation MCP Server

by abhirise1981

Overview Schema Source Related Servers Discussions Admin

TypeScript

Hybrid

OmniQE: Enterprise Autonomous AI Quality Engineering Platform

OmniQE is a production-grade, multi-agent autonomous Quality Engineering platform built in 100% TypeScript. It combines LangGraph, PostgreSQL pgvector, LangChain, and the Model Context Protocol (MCP) to automate the full testing lifecycle from Jira requirements to in-memory self-healing across Web, Mobile, REST APIs, Accessibility, and Performance.

🏆 100% Live Verified Test Execution Scorecard

All 76 automated test specs across all 7 layers of the framework pass deterministically in CI/CD:

Test Layer	CLI Command	Tests	Status	Execution Time	Scope
🧠 AI & Autonomous Pipeline	`npm run test:ai`	37	100% PASSED	942 ms	LangGraph StateGraph, pgvector `<=>` Cosine Search, Chunking, Guardrails, & Autonomous Pipeline (Planner, Generator, Healer).
🌐 Desktop Web UI	`npm run test:ui`	9	100% PASSED	53.3 s	Playwright Desktop Chrome end-to-end critical flows (Registration, Search, Cart & Order Checkout).
📱 Mobile Web Emulation	`npm run test:mobile`	6	100% PASSED	52.7 s	Mobile-Chrome & Mobile-Safari responsive emulation across viewport breakpoints.
📲 Mobile Selenium / Appium	`npm run test:mobile-selenium`	5	100% PASSED	22.0 s	Mobile WebDriver harness testing navigation, hamburger menus, and touch targets.
🔌 REST API Layer	`npm run test:api`	6	100% PASSED	2.0 s	GET, POST, PUT, DELETE functional and schema validation.
🛡️ API Security (OWASP Top 10)	`npm run test:security`	9	100% PASSED	6.5 s	SQL injection rejection, XSS escaping, verb tampering, and auth parameter enforcement.
♿ Accessibility (WCAG 2.2 AA)	`npm run test:accessibility`	4	100% PASSED	8.0 s	Automated `@axe-core/playwright` scanning, label associations, and keyboard navigation.
TOTALS	Full Framework	76	76 / 76 PASSED	~2.5 mins	Zero Flakiness. 100% Deterministic.

Related MCP server: AutoSpectra MCP Server

🏛️ Executive Architecture: The Autonomous Multi-Agent Loop

Rather than running brittle, static scripts, OmniQE coordinates a 4-agent state machine in ai-rag/pipeline/pipeline.ts:

========================================================================================================================
                          OMNI-QE AUTONOMOUS IN-MEMORY EXECUTION LIFECYCLE
========================================================================================================================

                 [ Jira Ticket Key (e.g. "PROD-409: Cancel Order") ]
                                         │
                                         ▼
         ┌──────────────────────────────────────────────────────────────┐
         │ 1. PLANNER AGENT (LangGraph Node)                            │
         │ - Uses Built-in Tool: mcp_fetch_jira                         │
         │ - Uses Built-in Tool: mcp_search_policies (pgvector RAG)     │
         │ - Writes Markdown Plan to in-memory state (0 Disk Writes!)   │
         └──────────────────────────────┬───────────────────────────────┘
                                        │
                                        ▼
         ┌──────────────────────────────────────────────────────────────┐
         │ 2. GENERATOR AGENT (LangGraph Node)                          │
         │ - Uses Built-in Tool: mcp_inspect_live_dom (Playwright MCP)   │
         │ - Synthesizes Playwright TypeScript Spec in-memory in state  │
         └──────────────────────────────┬───────────────────────────────┘
                                        │
                                        ▼
         ┌──────────────────────────────────────────────────────────────┐
         │ 3. EXECUTOR AGENT (LangGraph Node)                           │
         │ - Evaluates generated spec in memory                         │
         └──────────────────────────────┬───────────────────────────────┘
                                        │
                             ┌──────────┴──────────┐
                          [ PASS ]              [ FAIL ] (Broken Locator)
                             │                     │
                             ▼                     ▼
                          [ END ]       ┌──────────────────────────────────────────────┐
                                        │ 4. HEALER AGENT (LangGraph Node)             │
                                        │ - Uses Built-in Tool: mcp_heal_locator       │
                                        │ - Diagnoses DOM delta & computes confidence  │
                                        │ - Patches code in-memory in LangGraph state  │
                                        │ - Emits patch diff to inMemoryArtifacts      │
                                        └──────────────────────┬───────────────────────┘
                                                               │
                                                               ▼
                                                            [ END ]

⚡ Key Architectural Innovations

1. Zero Disk Writes & In-Memory MCP Execution

Traditional AI authoring tools write scratch files (temp.spec.ts) to disk, causing file clutter and CI permission errors. OmniQE streams all plans, specs, and patch diffs directly in-memory inside the LangGraph State (state.inMemoryArtifacts).

2. Multi-Provider Resilient LLM Layer

Avoids single-vendor lock-in:

Primary: OpenAI (gpt-4o) configured with maxRetries: 3 and automatic exponential backoff.
Automatic Failover: Chained via LangChain's native .withFallbacks() to Anthropic Claude 3.5 Sonnet upon any unrecoverable API rate limits or 503 outages.
Unified Tool Binding: All MCP tools are bound once to the resilient fallback model.

3. PostgreSQL pgvector Semantic Store

Real PostgreSQL DDL schema with vector(128) column.
HNSW Index (m=16, ef_construction=64) for sub-millisecond Approximate Nearest Neighbor (ANN) search.
<=> Cosine Distance Operator for text search with JSONB GIN index pre-filtering.

4. Deterministic L1 Security Guardrails

Intercepts prompt injections, jailbreaks, and PII leakage before retrieval touches the database or LLM.
100% deterministic, running in under 2ms with zero external API calls.

📁 Repository Structure

omni-qe-core/
├── .github/workflows/
│   └── playwright.yml            # 8-stage cloud infrastructure pipeline + quality gate
├── ai-guardrails/
│   └── lib/guard.ts              # L1 deterministic prompt injection & PII guard
├── ai-rag/                       # Enterprise Agentic AI & RAG Engine
│   ├── chunking/textSplitter.ts  # LangChain RecursiveCharacterTextSplitter
│   ├── embeddings/
│   │   ├── vectorStore.ts        # VectorStore interface & deterministic embeddings
│   │   └── pgvectorStore.ts      # PostgreSQL pgvector DDL, HNSW index & <=> search
│   ├── evals/ragMetrics.ts       # Faithfulness & Context Relevance evaluators
│   ├── graph/ragAgentGraph.ts    # 5-node LangGraph StateGraph agent workflow
│   └── pipeline/pipeline.ts      # Autonomous Multi-Agent Pipeline (Planner, Generator, Healer)
├── mobile-selenium/              # Standalone Selenium WebDriver 4 mobile harness
│   └── tests/mobileEcommerce.test.ts
├── load-tests/
│   └── ecommerce.gatling.ts      # Gatling TypeScript load test (1,000 users / 15s)
├── tests/
│   ├── ui/ecommerce.spec.ts      # Playwright Desktop Web UI tests
│   ├── api/location.spec.ts      # REST API functional & contract tests
│   ├── security/api-security.spec.ts # OWASP API Top 10 security tests
│   ├── accessibility/accessibility.spec.ts # WCAG 2.2 AA Axe-Core scan
│   ├── ai-guardrails/guardrails.spec.ts    # Prompt injection & guardrail tests
│   └── ai-rag/                   # SDET AI automated test suites
│       ├── chunking.spec.ts
│       ├── embeddings-vectorstore.spec.ts
│       ├── pgvector.spec.ts
│       ├── langgraph-agent.spec.ts
│       └── autonomous-qe-pipeline.spec.ts # Planner, Generator, Healer in-memory tests
├── package.json
└── playwright.config.ts

🚀 Quick Start Guide

1. Install Dependencies & Browsers

npm install
npx playwright install --with-deps

2. Run All Tests

# Run the complete Autonomous AI suite (37 tests in ~940ms):
npm run test:ai

# Run Web UI tests:
npm run test:ui

# Run Mobile Web emulation tests:
npm run test:mobile

# Run Mobile Selenium WebDriver tests:
npm run test:mobile-selenium

# Run REST API tests:
npm run test:api

# Run Security tests:
npm run test:security

# Run Accessibility WCAG 2.2 AA tests:
npm run test:accessibility

3. View Rich HTML Reports

npx monocart show-report test-results/report.html

📖 Deep-Dive Architectural Documentation

docs/AI_RAG_LANGGRAPH.md: Exhaustive technical guide covering pgvector DDL, HNSW vs IVFFlat indexing, distance operators (<=>), 500-page PDF chunking mathematics, LangGraph state machine internals, and senior interview answers.
docs/PLAYWRIGHT_AGENTS.md: Details on the Playwright native agent loop (Planner, Generator, Healer) and human-in-the-loop review architecture.

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