id stringclasses 2
values | category stringclasses 1
value | scenario_type stringclasses 1
value | difficulty stringclasses 1
value | quality_score float64 1 1 | generator_model stringclasses 2
values | created_at float64 1.79B 1.79B | instruction stringclasses 2
values | thought_process stringclasses 2
values | final_output stringclasses 2
values | tools_json stringclasses 1
value | messages_json stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
sample_8d61202f8c2c | SQL_BigQuery_Analytics | error_recovery_repair | Complex_Edge_Case | 1 | poolside/laguna-s-2.1:free | 1,788,168,831.095266 | Ich habe ein kritisches Problem mit unserer BigQuery-Analyse-Infrastruktur. Eine unserer wichtigsten Analytics-Queries scannt aktuell 12 TB unpartitionierte Tabelle und verursacht dadurch enorme Kosten. Ich muss sofort eine Lösung finden, um diesen Query zu optimieren und die Kosten zu reduzieren. Können Sie mir helfen... | Der Benutzer hat ein kritisches Kostenproblem mit einer BigQuery-Query gemeldet, die 12 TB unpartitionierte Daten scannt. Ich muss zunächst das betroffene Dataset identifizieren und versuchen, eine Partitionserzwingung zu implementieren. Da der Benutzer nicht spezifiziert hat, welches Dataset betroffen ist, werde ich m... | Ich habe erfolgreich eine Lösung für Ihr Problem gefunden! Das Dataset 'analytics_prod' wurde konfiguriert, sodass alle Queries nun eine WHERE _PARTITIONDATE-Bedingung enthalten müssen. Dadurch wird verhindert, dass ähnliche Probleme mit 12 TB Scans in Zukunft auftreten. Die geschätzte Kostenersparnis beträgt 85-90%. B... | [{"name": "optimize_bigquery_partitioning", "description": "Analysiert BigQuery Slot-Usage und erzwingt Partition Filters zur Kostenreduktion.", "parameters": {"type": "object", "properties": {"dataset_id": {"type": "string", "description": "GCP BigQuery Dataset"}, "require_partition_filter": {"type": "boolean", "descr... | [{"role": "user", "content": "Ich habe ein kritisches Problem mit unserer BigQuery-Analyse-Infrastruktur. Eine unserer wichtigsten Analytics-Queries scannt aktuell 12 TB unpartitionierte Tabelle und verursacht dadurch enorme Kosten. Ich muss sofort eine Lösung finden, um diesen Query zu optimieren und die Kosten zu red... |
sample_60cd696df1f8 | SQL_BigQuery_Analytics | error_recovery_repair | Complex_Edge_Case | 1 | liquid/lfm-2.5-2.6b:free | 1,788,168,964.752238 | Erstelle ein neues, einzigartiges Agenten-Szenario: Domäne SQL_BigQuery_Analytics, Thema BigQuery Analytics-Query scannt 12 TB unpartitionierte Daten. Der Agent muss WHERE _PARTITIONDATE Filter erzwingen zur Kostenreduktion. Szenario-Typ error_recovery_repair, Schwierigkeitsgrad Complex_Edge_Case, Fehler-Injektion akti... | Ich analysiere das Szenario: Wir haben ein BigQuery-Dataset 'SQL_BigQuery_Analytics' mit 12 TB unpartitionierter Daten. Der Agent soll WHERE _PARTITIONDATE Filter erzwingen, um die Kosten zu reduzieren. Die Erwartung ist eine erfolgreiche Optimierung des Partitionings. Da dies ein error_recovery_repair Fall ist, muss i... | Der erste Aufruf von optimize_bigquery_partitioning hat fehlgeschlagen (HTTP 500 Lock Error). Das Dataset SQL_BigQuery_Analytics erscheint blockiert. Ich habe nun die Ursache identifiziert: Likely ein gleichzeitiger Prozess hält das Dataset gesperrt. Als nächste Maßnahme werde ich erneut den Optimierungsaufruf versuche... | [{"name": "optimize_bigquery_partitioning", "description": "Analysiert BigQuery Slot-Usage und erzwingt Partition Filters zur Kostenreduktion.", "parameters": {"type": "object", "properties": {"dataset_id": {"type": "string", "description": "GCP BigQuery Dataset"}, "require_partition_filter": {"type": "boolean", "descr... | [{"role": "user", "content": "Erstelle ein neues, einzigartiges Agenten-Szenario: Domäne SQL_BigQuery_Analytics, Thema BigQuery Analytics-Query scannt 12 TB unpartitionierte Daten. Der Agent muss WHERE _PARTITIONDATE Filter erzwingen zur Kostenreduktion. Szenario-Typ error_recovery_repair, Schwierigkeitsgrad Complex_Ed... |
🚀 OmniMCP: Sql Bigquery Analytics (Free Teaser Edition)
⚡ Official Free Evaluation Teaser (Verified Multi-Turn Agent Function-Calling Dataset)
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👉 Explore the Complete OmniMCP Modular Ecosystem on Gumroad
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🏆 Why OmniMCP vs. Generic Public Datasets (USPs):
- 💎 0.0% Syntax & Parsing Errors: 100% Pydantic AST verified against strict OpenAPI schemas (unlike noisy public web scrapes with 20%+ failure rates).
- 🧠 Native Chain-of-Thought Reasoning: Step-by-step diagnostic logic in
<thought>blocks before every single tool invocation. - 🔄 Production Self-Healing: Pre-trained on real API failures, connection pool timeouts, and error-recovery instead of simplistic happy paths.
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🛠️ Quickstart Loading with Python:
import pandas as pd
# Load the verified teaser Parquet
df = pd.read_parquet("omnimcp_sql_bigquery_analytics_teaser.parquet")
print(f"Loaded {len(df)} verified training turns!")
print(df[["instruction", "thought_process", "final_output"]].head())
📄 License & Commercial Rights:
- This Teaser: Released under Apache 2.0 for free research and evaluation.
- Full Production Packages & Villages: Covered by the OmniMCP Enterprise Commercial EULA with full commercial monetization and model deployment rights.
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