Feature 1: The MCP Característica 1: El MCP

AI reads your code.
Playwright tests, done.
La IA lee tu código.
Pruebas Playwright listas.

500 PRs a week. No time for tickets or acceptance criteria. The Test Architect MCP scans your codebase, generates Playwright tests automatically, runs them, and sends results to QA Hub. The source of truth is the code itself. 500 PRs semanales. Sin tiempo para tickets. El MCP Test Architect escanea tu código, genera pruebas Playwright automáticamente, las ejecuta y envía los resultados a QA Hub. El código es la fuente de la verdad.

          flowchart TD
            subgraph Your Infrastructure
              A[(Codebase)]
            end
            
            B{Test Architect MCP}
            C((Playwright Tests))
            
            subgraph QA Hub Platform
              D[Dashboard & Analytics]
            end
            
            A -->|1. Scans Context| B
            B -->|2. Generates & Executes| C
            C -->|3. CLI Posts Results| D
            
            style B fill:#ff5f56,stroke:#fff,stroke-width:2px,color:#fff
            style C fill:#1e1e2e,stroke:#ff5f56,stroke-width:2px,color:#fff,stroke-dasharray: 5 5
            style D fill:#8b5cf6,stroke:#fff,stroke-width:2px,color:#fff
        
Feature 2: AI BDD Engine Característica 2: Motor IA BDD

Your tickets become a test suite.
Automatically.
Tus tickets se vuelven pruebas.
Automáticamente.

Connect Jira, Linear, or Azure DevOps. QA Hub's Engine analyzes the acceptance criteria and generates ISTQB-standard BDD test cases. Your central test library stays perfectly in sync as your tickets evolve. Conecta Jira, Linear o Azure DevOps. El motor de QA Hub analiza los criterios de aceptación y genera pruebas BDD bajo estándar ISTQB. Tu librería central se mantiene sincronizada mientras los tickets evolucionan.

          flowchart TD
            A[Jira / Linear / ADO] -->|1. Webhook / API Sync| B{QA Hub AI Engine}
            
            subgraph QA Hub Processing
              B -->|2. Analyzes Criteria| C[ISTQB Rules Engine]
              C -->|3. Generates BDD| D((Draft Test Scenarios))
            end
            
            D -->|4. Saves to| E[(Central Test Case Library)]
            
            style B fill:#4d8bff,stroke:#fff,stroke-width:2px,color:#fff
            style C fill:#1e1e2e,stroke:#4d8bff,stroke-width:2px,color:#fff
            style E fill:#8b5cf6,stroke:#fff,stroke-width:2px,color:#fff
        
Feature 3: Bug Loop Característica 3: Ciclo de Bugs

Results everyone reads.
Auto-queued retests.
Resultados para todos.
Retesteos automáticos.

One CLI command uploads results from Playwright, Cypress, or Jest. When a failed test is linked to a bug, and the developer marks it "Done" in Linear, QA Hub automatically queues it for a retest. Sube resultados con la CLI de Playwright, Cypress o Jest. Si un test fallido tiene un bug, y el developer lo marca "Hecho" en Linear, QA Hub lo encola automáticamente para re-testeo.

          flowchart TD
            A((Failed Test Result)) -->|1. QA Links Bug| B[Linear / Jira Ticket]
            B -->|2. Dev Fixes & Marks 'Done'| C{Webhook Trigger}
            C -->|3. Status = Done| D[QA Hub Retest Queue]
            D -->|4. Auto-execute Test| E((Playwright / Cypress))
            E -->|5. Uploads New Result| A
            
            style A fill:#ff5f56,stroke:#fff,stroke-width:2px,color:#fff
            style D fill:#8b5cf6,stroke:#fff,stroke-width:2px,color:#fff
            style B fill:#1e1e2e,stroke:#fff,stroke-width:1px,color:#fff
            style C fill:#4d8bff,stroke:#fff,stroke-width:2px,color:#fff
        
Feature 4: QA Scout

Ask your QA data.
Get straight answers.

QA Scout is an AI agent embedded directly in QA Hub. It has access to your real test cases, run results, and metrics. Ask it anything within QA scope and it calls the right tool, fetches the data, and responds. No off-topic. No hallucinations. Scope-bounded by design.

graph LR Q([You ask a question]) --> S[QA Scout] S -->|searchTestCases| DB1[(Test Cases)] S -->|getRecentFailures| DB2[(Run Results)] S -->|getMetrics| DB3[(Metrics)] S -->|compareTestCases| DB4[(Versions)] DB1 & DB2 & DB3 & DB4 --> A([Clear answer in your language]) style S fill:#6366f1,color:#fff,stroke:#4f46e5 style Q fill:#1e293b,color:#e2e8f0,stroke:#334155 style A fill:#1e293b,color:#e2e8f0,stroke:#334155