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Quickstart Guide

Get your first test running in under 5 minutes.

Step 1: Start the Services

docker compose up --build

Wait for all services to start (you'll see health checks passing in the logs): - mcp-server on port 8080 - command-center on port 3000 - redis on port 6379 - worker-pool ready for distributed tasks

Step 2: Open the Dashboard

Navigate to http://localhost:3000 in your browser.

You'll see the Mission Control dashboard with: - Live agent count and status - Test launcher form - Real-time metrics via Server-Sent Events - Chat widget for conversational test configuration

Step 3: Launch Your First Test

Method A: Direct Feature Test (Fastest)

Test specific features without spawning agents:

from mcp_server.tools import execute_tool

# Test performance
result = execute_tool("test_performance", {
    "url": "https://example.com",
    "thresholds": {"lcp_ms": 2500}
})

print(f"TTFB: {result['metrics']['ttfb_ms']}ms")
print(f"Status: {result['status']}")

Method B: Feature Test via Dashboard

  1. In the Launch Test panel, enter a URL: 

    https://example.com

  2. Select Test Type:

  3. Performance — Core Web Vitals
  4. Accessibility — WCAG compliance
  5. Auth Flow — Login/logout security
  6. Visual Regression — Screenshot comparison
  7. API Contract — Schema validation

  8. Multi-Browser — Cross-browser smoke test

  9. Click ▶ Initiate

  1. Click the 🤖 Vectra chat widget (bottom-right corner)
  2. Type: 
    Test the homepage of https://example.com for performance and accessibility
  3. Vectra will confirm the plan — click ✓ Run

Method D: Spawn an Agent (Full Exploration)

For comprehensive testing with AI-driven exploration:

result = execute_tool("spawn_agent", {
    "role": "ui_explorer",
    "objective": "Explore https://example.com and test all interactive elements",
    "memory_node": "Runs/Full_Exploration.md"
})

Step 4: Monitor Progress

Watch the dashboard update in real-time: - Agent appears in the Active Agents grid - Progress bar fills as tests run - Console log shows live output - Terminal panel streams events

Step 5: View Results

When the test completes: 1. The agent card turns green (pass), yellow (warning), or red (fail) 2. Click View Result to see detailed findings 3. Or ask Vectra: "What did the last test find?"

Results Structure

All feature tests return a standardized result:

{
  "status": "pass|warning|fail",
  "findings": [
    {
      "title": "Slow TTFB",
      "description": "Time to First Byte: 850ms (threshold: 600ms)",
      "severity": "high"
    }
  ],
  "metrics": {
    "ttfb_ms": 850,
    "fcp_ms": 1200,
    "lcp_ms": 2400
  },
  "duration_seconds": 5.2,
  "timestamp": "2026-06-01T12:00:00Z"
}

What Happens Behind the Scenes?

sequenceDiagram
    participant User
    participant Dashboard
    participant CommandCenter
    participant MCPServer
    participant Agent
    participant Vault
    participant Redis

    User->>Dashboard: Enter URL + test type
    Dashboard->>CommandCenter: POST /api/tests/run
    CommandCenter->>MCPServer: execute_tool or spawn_agent
    MCPServer->>Redis: Cache LLM responses
    MCPServer->>Vault: Create memory node
    MCPServer->>Agent: Start worker process
    Agent->>Vault: Update progress
    Vault->>CommandCenter: File change detected
    CommandCenter->>Dashboard: SSE update
    Agent->>Vault: Write findings
    Agent->>Vault: Set status=completed
    CommandCenter->>Dashboard: Test complete

Quick Examples

Authentication Test

result = execute_tool("test_auth_flow", {
    "login_url": "https://example.com/login",
    "username": "test@example.com",
    "password": "password123",
    "logout_url": "https://example.com/logout"
})

Accessibility Test

result = execute_tool("test_accessibility", {
    "url": "https://example.com",
    "standard": "wcag2aa"
})

Visual Regression Test

result = execute_tool("test_visual_regression", {
    "url": "https://example.com",
    "name": "homepage"
})

Next Steps

Common Commands

# View agent logs
docker logs vectra-mcp-server

# View worker pool logs
docker logs vectra-worker-pool

# Scale worker pool (more parallel agents)
docker compose up -d --scale worker-pool=3

# Restart a service
docker compose restart command-center

# Stop all services
docker compose down

# Clean restart (removes volumes)
docker compose down -v && docker compose up --build

# Run tests inside container
docker compose exec mcp-server pytest tests/ -v