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Ariel Integration Architecture

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Overview

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Architecture

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Data Flow

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Baseline Creation Flow

1. User invokes ariel_baseline_create MCP tool or REST API
2. Auth bridge validates user context and workspace
3. Repository factory selects YAML or PostgreSQL backend
4. BaselineManager creates draft baseline
5. CIs are added via ariel_baseline_add_ci
6. ariel_baseline_freeze computes hashes and sets status

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Sync Flow

1. On freeze, SyncEngine maps baseline → Traceo requirements
2. Creates/updates AR-prefixed requirements in Traceo
3. Links Traceo references as relationships
4. Logs sync history to ariel_sync_history table

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Module Structure

traceo_mcp_server/ariel_integration/
├── __init__.py
├── auth_bridge.py          # Traceo → Ariel auth conversion
├── events.py               # Traceo-side events
├── models.py               # Pydantic DB ↔ Ariel conversion
├── mcp_tools.py            # 10 MCP tools
├── repositories/
│   ├── baseline_repo.py    # PostgreSQL backend
│   ├── ci_repo.py          # CI-level operations
│   └── factory.py          # Backend selection
└── sync/
    ├── engine.py            # Sync orchestrator
    ├── baseline_to_traceo.py
    ├── traceo_to_baseline.py
    └── conflict_resolver.py

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MCP Tools

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Database Tables

• ariel_baselines — Baseline metadata (workspace-scoped)
• ariel_configuration_items — CIs per baseline
• ariel_traceo_references — Links to Traceo requirements
• ariel_sync_history — Sync operation log

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Configuration