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fix: replace 'zero false positives' with 'high precision (avg 0.90)' per review

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Greg Partin
2026-02-24 20:36:30 -08:00
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@@ -826,7 +826,7 @@ Integrations and tools designed to simplify data exploration, analysis and enhan
- [DataEval/dingo](https://github.com/DataEval/dingo) 🎖️ 🐍 🏠 🍎 🪟 🐧 - MCP server for the Dingo: a comprehensive data quality evaluation tool. Server Enables interaction with Dingo's rule-based and LLM-based evaluation capabilities and rules&prompts listing.
- [datalayer/jupyter-mcp-server](https://github.com/datalayer/jupyter-mcp-server) 🐍 🏠 - Model Context Protocol (MCP) Server for Jupyter.
- [growthbook/growthbook-mcp](https://github.com/growthbook/growthbook-mcp) 🎖️ 📇 🏠 🪟 🐧 🍎 — Tools for creating and interacting with GrowthBook feature flags and experiments.
- [gpartin/WaveGuardClient](https://github.com/gpartin/WaveGuardClient) 🐍 ☁️ 🍎 🪟 🐧 - Physics-based anomaly detection via MCP. Uses Klein-Gordon wave equations on GPU to detect anomalies with zero false positives in benchmarks. 3 tools: scan vectors, scan time-series, health check.
- [gpartin/WaveGuardClient](https://github.com/gpartin/WaveGuardClient) 🐍 ☁️ 🍎 🪟 🐧 - Physics-based anomaly detection via MCP. Uses Klein-Gordon wave equations on GPU to detect anomalies with high precision (avg 0.90) in benchmarks. 3 tools: scan vectors, scan time-series, health check.
- [HumanSignal/label-studio-mcp-server](https://github.com/HumanSignal/label-studio-mcp-server) 🎖️ 🐍 ☁️ 🪟 🐧 🍎 - Create, manage, and automate Label Studio projects, tasks, and predictions for data labeling workflows.
- [jjsantos01/jupyter-notebook-mcp](https://github.com/jjsantos01/jupyter-notebook-mcp) 🐍 🏠 - connects Jupyter Notebook to Claude AI, allowing Claude to directly interact with and control Jupyter Notebooks.
- [kdqed/zaturn](https://github.com/kdqed/zaturn) 🐍 🏠 🪟 🐧 🍎 - Link multiple data sources (SQL, CSV, Parquet, etc.) and ask AI to analyze the data for insights and visualizations.