Protokoll #24917

ID24917
Zeitstempel2025-12-28 01:30:01.885365
Clientroot
IP145.224.72.140
Modellclaude-sonnet-4-20250514
Statuscompleted
Tokens2,474 (Input: 1,163, Output: 1,311)
Dauer341 ms
Request-Zeit2025-12-28 01:30:01.885365
Response-Zeit2025-12-28 01:30:02.226737

Request

{
    "event": "PreToolUse",
    "tool_name": "mcp__mcp-tasks__tasks_create",
    "tool_input": {
        "title": "Pipeline-Refactoring 1\/6: DB-Schema für Textsemantik und Wissenssemantik",
        "type": "ai_task",
        "parent_task_id": 492,
        "description": "## Ziel\nZwei neue Tabellen in ki_content erstellen für die semantischen Analyse-Ergebnisse.\n\n---\n\n## Analyse\n\n### Textsemantik (Phase 4)\nAnalysiert WIE etwas gesagt wird, bevor wir wissen WAS gesagt wird.\n\n**Dimensionen:**\n| Dimension | Werte | Beispiel |\n|-----------|-------|----------|\n| statement_form | assertion, question, command, conditional | \"Systemische Therapie ist...\" → assertion |\n| intent | explain, argue, define, compare, contrast, exemplify, warn | \"erklärt\" → explain |\n| frame | theoretical, practical, historical, methodological, critical | Kontext der Aussage |\n| negation | true\/false | \"ist NICHT geeignet\" → true |\n| discourse_role | thesis, evidence, example, counter, transition, summary | Rolle im Text |\n\n### Wissenssemantik (Phase 6)\nAnalysiert die BEDEUTUNG der Entitäten im spezifischen Kontext.\n\n**Dimensionen:**\n| Dimension | Werte | Beispiel |\n|-----------|-------|----------|\n| role | agent, patient, instrument, location, cause, effect | Wer\/was tut? |\n| properties | JSON | {\"duration\": \"6 Monate\", \"setting\": \"ambulant\"} |\n| functional_category | method, tool, concept, actor, outcome, constraint | Was IST es? |\n| context_meaning | Text | Spezifische Bedeutung hier |\n\n---\n\n## Planung\n\n### Tabelle 1: chunk_text_semantics\n```sql\nCREATE TABLE chunk_text_semantics (\n    id INT AUTO_INCREMENT PRIMARY KEY,\n    chunk_id INT NOT NULL,\n    \n    -- Aussageform\n    statement_form ENUM('assertion', 'question', 'command', 'conditional', 'exclamation') NOT NULL,\n    \n    -- Kommunikative Absicht\n    intent ENUM('explain', 'argue', 'define', 'compare', 'contrast', 'exemplify', \n                'warn', 'recommend', 'describe', 'narrate', 'instruct') DEFAULT NULL,\n    \n    -- Referenzrahmen\n    frame ENUM('theoretical', 'practical', 'historical', 'methodological', \n               'critical', 'empirical', 'normative') DEFAULT NULL,\n    \n    -- Negation\n    is_negated BOOLEAN DEFAULT FALSE,\n    \n    -- Diskursrolle\n    discourse_role ENUM('thesis', 'evidence', 'example', 'counter', 'transition', \n                        'summary', 'definition', 'elaboration', 'conclusion') DEFAULT NULL,\n    \n    -- Qualität\n    confidence DECIMAL(3,2) DEFAULT NULL,\n    model_used VARCHAR(100) DEFAULT NULL,\n    \n    -- Zeitstempel\n    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,\n    \n    -- Constraints\n    FOREIGN KEY (chunk_id) REFERENCES chunks(id) ON DELETE CASCADE,\n    INDEX idx_chunk (chunk_id),\n    INDEX idx_intent (intent),\n    INDEX idx_discourse (discourse_role)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;\n```\n\n### Tabelle 2: entity_knowledge_semantics\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,              -- Kontext der Bedeutung\n    \n    -- Semantische Rolle\n    semantic_role ENUM('agent', 'patient', 'instrument', 'location', 'cause', \n                       'effect', 'beneficiary', 'source', 'goal', 'theme') DEFAULT NULL,\n    \n    -- Eigenschaften (flexibel)\n    properties JSON DEFAULT NULL,\n    \n    -- Funktionale Kategorie\n    functional_category ENUM('method', 'tool', 'concept', 'actor', 'outcome', \n                             'constraint', 'resource', 'process', 'artifact', 'principle') DEFAULT NULL,\n    \n    -- Kontextuelle Bedeutung\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    -- Zeitstempel\n    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,\n    \n    -- Constraints\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    INDEX idx_role (semantic_role),\n    INDEX idx_category (functional_category)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;\n```\n\n---\n\n## Abhängigkeiten\n- Keine (erste Aufgabe)\n\n## Akzeptanzkriterien\n- [ ] Beide Tabellen existieren in ki_content\n- [ ] Foreign Keys funktionieren\n- [ ] Indizes sind angelegt\n- [ ] Test-Insert erfolgreich"
    }
}

Response

{
    "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 erstellen für die semantischen Analyse-Ergebnisse.\\n\\n---\\n\\n## Analyse\\n\\n### Textsemantik (Phase 4)\\nAnalysiert WIE etwas gesagt wird, bevor wir wissen WAS gesagt wird.\\n\\n**Dimensionen:**\\n| Dimension | Werte | Beispiel |\\n|-----------|-------|----------|\\n| statement_form | assertion, question, command, conditional | \\\"Systemische Therapie ist...\\\" → assertion |\\n| intent | explain, argue, define, compare, contrast, exemplify, warn | \\\"erklärt\\\" → explain |\\n| frame | theoretical, practical, historical, methodological, critical | Kontext der Aussage |\\n| negation | true\/false | \\\"ist NICHT geeignet\\\" → true |\\n| discourse_role | thesis, evidence, example, counter, transition, summary | Rolle im Text |\\n\\n### Wissenssemantik (Phase 6)\\nAnalysiert die BEDEUTUNG der Entitäten im spezifischen Kontext.\\n\\n**Dimensionen:**\\n| Dimension | Werte | Beispiel |\\n|-----------|-------|----------|\\n| role | agent, patient, instrument, location, cause, effect | Wer\/was tut? |\\n| properties | JSON | {\\\"duration\\\": \\\"6 Monate\\\", \\\"setting\\\": \\\"ambulant\\\"} |\\n| functional_category | method, tool, concept, actor, outcome, constraint | Was IST es? |\\n| context_meaning | Text | Spezifische Bedeutung hier |\\n\\n---\\n\\n## Planung\\n\\n### Tabelle 1: chunk_text_semantics\\n```sql\\nCREATE TABLE chunk_text_semantics (\\n    id INT AUTO_INCREMENT PRIMARY KEY,\\n    chunk_id INT NOT NULL,\\n    \\n    -- Aussageform\\n    statement_form ENUM('assertion', 'question', 'command', 'conditional', 'exclamation') NOT NULL,\\n    \\n    -- Kommunikative Absicht\\n    intent ENUM('explain', 'argue', 'define', 'compare', 'contrast', 'exemplify', \\n                'warn', 'recommend', 'describe', 'narrate', 'instruct') DEFAULT NULL,\\n    \\n    -- Referenzrahmen\\n    frame ENUM('theoretical', 'practical', 'historical', 'methodological', \\n               'critical', 'empirical', 'normative') DEFAULT NULL,\\n    \\n    -- Negation\\n    is_negated BOOLEAN DEFAULT FALSE,\\n    \\n    -- Diskursrolle\\n    discourse_role ENUM('thesis', 'evidence', 'example', 'counter', 'transition', \\n                        'summary', 'definition', 'elaboration', 'conclusion') DEFAULT NULL,\\n    \\n    -- Qualität\\n    confidence DECIMAL(3,2) DEFAULT NULL,\\n    model_used VARCHAR(100) DEFAULT NULL,\\n    \\n    -- Zeitstempel\\n    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\\n    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,\\n    \\n    -- Constraints\\n    FOREIGN KEY (chunk_id) REFERENCES chunks(id) ON DELETE CASCADE,\\n    INDEX idx_chunk (chunk_id),\\n    INDEX idx_intent (intent),\\n    INDEX idx_discourse (discourse_role)\\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;\\n```\\n\\n### Tabelle 2: entity_knowledge_semantics\\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,              -- Kontext der Bedeutung\\n    \\n    -- Semantische Rolle\\n    semantic_role ENUM('agent', 'patient', 'instrument', 'location', 'cause', \\n                       'effect', 'beneficiary', 'source', 'goal', 'theme') DEFAULT NULL,\\n    \\n    -- Eigenschaften (flexibel)\\n    properties JSON DEFAULT NULL,\\n    \\n    -- Funktionale Kategorie\\n    functional_category ENUM('method', 'tool', 'concept', 'actor', 'outcome', \\n                             'constraint', 'resource', 'process', 'artifact', 'principle') DEFAULT NULL,\\n    \\n    -- Kontextuelle Bedeutung\\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    -- Zeitstempel\\n    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\\n    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,\\n    \\n    -- Constraints\\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    INDEX idx_role (semantic_role),\\n    INDEX idx_category (functional_category)\\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;\\n```\\n\\n---\\n\\n## Abhängigkeiten\\n- Keine (erste Aufgabe)\\n\\n## Akzeptanzkriterien\\n- [ ] Beide Tabellen existieren in ki_content\\n- [ ] Foreign Keys funktionieren\\n- [ ] Indizes sind angelegt\\n- [ ] Test-Insert erfolgreich\",\n    \"type\": \"ai_task\",\n    \"status\": \"pending\",\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\": \"2025-12-28T01:30:02.004004\",\n    \"completed_at\": null,\n    \"metadata\": {}\n  },\n  \"message\": \"Task #493 created\"\n}"
        }
    ]
}
← Vorheriger Zur Liste Nächster →