id string | category string | scenario_type string | difficulty string | quality_score float64 | generator_model string | created_at float64 | instruction string | thought_process string | final_output string | tools_json string | messages_json string |
|---|---|---|---|---|---|---|---|---|---|---|---|
sample_ce8e218f08f0 | Python_Docker_Pytest | multi_tool_chain | Complex_Edge_Case | 0.9 | nvidia/nemotron-3-super-120b-a12b:free | 1,788,169,290.076581 | Führe die Testsuite im Docker-Container aus, stelle sicher dass die Gesamt-Coverage mindestens 90% erreicht und identifiziere die async Fixtures, die diesen Schwellenwert verfehlen, damit sie gezielt verbessert werden können. | Erster Schritt: Führe Pytest mit einer Coverage-Schwelle von 90% aus, um den aktuellen Stand zu ermitteln und festzustellen, ob die Schwelle bereits verletzt ist. Dabei soll das Test-Container nicht beim ersten Fehler abgebrochen werden, damit wir die vollständige Coverage-Information erhalten. | Ergebnis:\n- Gesamt-Coverage aller Tests: 84% (unter dem geforderten 90%-Schwelle).\n- Zwei async Fixtures verursachen den Defizit: `test_order_creation_fixture` und `test_payment_processing_fixture` in `tests/test_async_fixture.py`.\n- Die Fixture `test_order_creation_fixture` schlägt fehl, weil ein erwarteter Order-S... | [{"name": "run_pytest_suite_in_docker", "description": "Führt Pytest-Tests mit pytest-asyncio in isoliertem Test-Container aus.", "parameters": {"type": "object", "properties": {"test_file_path": {"type": "string", "description": "Pfad zur Testdatei (z.B. tests/test_orders.py)"}, "coverage_threshold_pct": {"type": "int... | [{"role": "user", "content": "Führe die Testsuite im Docker-Container aus, stelle sicher dass die Gesamt-Coverage mindestens 90% erreicht und identifiziere die async Fixtures, die diesen Schwellenwert verfehlen, damit sie gezielt verbessert werden können.", "thought_process": "", "tool_calls": null, "tool_response": nu... |
🚀 OmniMCP: Python Docker Pytest (Free Teaser Edition)
⚡ Official Free Evaluation Teaser (Verified Multi-Turn Agent Function-Calling Dataset)
🏆 Get the Full Production Master Package & Commercial EULA License on Gumroad:
👉 Explore the Complete OmniMCP Modular Ecosystem on Gumroad
🏛️ The Modular OmniMCP Architecture: How Bricks, Houses & Villages Connect
Every OmniMCP dataset is engineered as a precision-fit building block within a comprehensive, multi-department enterprise AI hierarchy:
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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.
- 🛡️ 100% GDPR / DSGVO Clean: Zero personal data (RFC 2606
@example.com, RFC 5737 sanitized documentation IPs). - ⚖️ EU AI Act & EULA Ready: Includes official Articles 50 & 53 synthetic provenance declaration.
🛠️ Quickstart Loading with Python:
import pandas as pd
# Load the verified teaser Parquet
df = pd.read_parquet("omnimcp_python_docker_pytest_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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