{
"tool_response": [
{
"type": "text",
"text": "{\n \"success\": true,\n \"task\": {\n \"id\": 493,\n \"uuid\": \"23345776-c1bc-4e62-8093-53ac508ddd42\",\n \"title\": \"Pipeline-Refactoring 1\/6: DB-Schema für Textsemantik und Wissenssemantik\",\n \"description\": \"## Ziel\\nZwei neue Tabellen in ki_content für semantische Analyse-Ergebnisse.\\n\\n---\\n\\n## ⚠️ PFLICHT VOR ARBEIT\\n\\n### 1. CLAUDE.md lesen\\n```bash\\ncat \/var\/www\/CLAUDE.md\\n```\\n\\n### 2. Bestehende Tabellen analysieren\\n```\\nmcp__mcp-db__db_tables(\\\"ki_content\\\")\\nmcp__mcp-db__db_describe(\\\"chunks\\\", \\\"ki_content\\\")\\nmcp__mcp-db__db_describe(\\\"entities\\\", \\\"ki_content\\\")\\n```\\n\\n### 3. Naming-Konventionen prüfen\\n- Bestehende Tabellen: snake_case\\n- Foreign Keys: `{table}_id`\\n- Timestamps: `created_at`, `updated_at`\\n\\n---\\n\\n## Code-Prinzipien\\n\\n| Prinzip | Anwendung |\\n|---------|-----------|\\n| **DRY** | Wiederverwendbare ENUM-Werte |\\n| **KISS** | Nur nötige Spalten, kein Over-Engineering |\\n| **SRP** | Eine Tabelle = ein Konzept |\\n| **YAGNI** | Keine spekulativen Spalten |\\n\\n---\\n\\n## Tabelle 1: chunk_text_semantics\\n\\n```sql\\nCREATE TABLE chunk_text_semantics (\\n id INT AUTO_INCREMENT PRIMARY KEY,\\n chunk_id INT NOT NULL,\\n \\n -- Aussageform: WIE wird etwas gesagt?\\n statement_form ENUM('assertion', 'question', 'command', 'conditional') NOT NULL,\\n intent ENUM('explain', 'argue', 'define', 'compare', 'exemplify', 'warn', 'instruct') DEFAULT NULL,\\n frame ENUM('theoretical', 'practical', 'historical', 'methodological', 'critical') DEFAULT NULL,\\n is_negated BOOLEAN DEFAULT FALSE,\\n discourse_role ENUM('thesis', 'evidence', 'example', 'counter', 'summary', 'definition') DEFAULT NULL,\\n \\n -- Qualität\\n confidence DECIMAL(3,2) DEFAULT NULL,\\n model_used VARCHAR(100) DEFAULT NULL,\\n \\n -- Timestamps\\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\\n updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,\\n \\n FOREIGN KEY (chunk_id) REFERENCES chunks(id) ON DELETE CASCADE,\\n INDEX idx_chunk (chunk_id),\\n INDEX idx_intent (intent)\\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;\\n```\\n\\n## Tabelle 2: entity_knowledge_semantics\\n\\n```sql\\nCREATE TABLE entity_knowledge_semantics (\\n id INT AUTO_INCREMENT PRIMARY KEY,\\n entity_id INT NOT NULL,\\n chunk_id INT NOT NULL,\\n \\n -- Bedeutung: WAS bedeutet die Entität hier?\\n semantic_role ENUM('agent', 'patient', 'instrument', 'location', 'cause', 'effect') DEFAULT NULL,\\n properties JSON DEFAULT NULL,\\n functional_category ENUM('method', 'tool', 'concept', 'actor', 'outcome', 'process') DEFAULT NULL,\\n context_meaning TEXT DEFAULT NULL,\\n \\n -- Qualität\\n confidence DECIMAL(3,2) DEFAULT NULL,\\n model_used VARCHAR(100) DEFAULT NULL,\\n \\n -- Timestamps\\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\\n updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,\\n \\n FOREIGN KEY (entity_id) REFERENCES entities(id) ON DELETE CASCADE,\\n FOREIGN KEY (chunk_id) REFERENCES chunks(id) ON DELETE CASCADE,\\n INDEX idx_entity (entity_id),\\n INDEX idx_chunk (chunk_id)\\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;\\n```\\n\\n---\\n\\n## Ausführung via MCP\\n\\n```\\nmcp__mcp-db__db_execute(statement=\\\"CREATE TABLE...\\\", database=\\\"ki_content\\\")\\n```\\n\\n**KEIN** direkter SQL-Zugriff!\\n\\n---\\n\\n## Akzeptanzkriterien\\n- [ ] CLAUDE.md gelesen und verstanden\\n- [ ] Bestehende Tabellen analysiert\\n- [ ] Beide Tabellen via MCP erstellt\\n- [ ] Foreign Keys funktionieren\\n- [ ] Test-INSERT erfolgreich\",\n \"type\": \"ai_task\",\n \"status\": \"in_progress\",\n \"created_by\": \"mcp-tasks\",\n \"created_by_type\": \"ai\",\n \"parent_task_id\": 492,\n \"due_date\": null,\n \"created_at\": \"2025-12-28T01:30:02.004003\",\n \"updated_at\": \"2025-12-28T01:48:20.042873\",\n \"completed_at\": null,\n \"metadata\": {}\n },\n \"message\": \"Task #493 status changed: pending -> in_progress\"\n}"
}
]
}