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@@ -322,6 +322,7 @@ Web 内容访问和自动化功能。支持以 AI 友好格式搜索、抓取和
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- [@reading-plus-ai/mcp-server-data-exploration](https://github.com/reading-plus-ai/mcp-server-data-exploration) 🐍 ☁️ - 支持对基于 .csv 的数据集进行自主数据探索,以最小的成本提供智能见解。
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- [zcaceres/markdownify-mcp](https://github.com/zcaceres/markdownify-mcp) 📇 🏠 - 一个 MCP 服务器,可将几乎任何文件或网络内容转换为 Markdown
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- [@reading-plus-ai/mcp-server-data-exploration](https://github.com/reading-plus-ai/mcp-server-data-exploration) 🐍 ☁️ - 实现基于.csv数据集的自动数据探索,提供最少工作量的智能化洞察。
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- [DataEval/dingo](https://github.com/DataEval/dingo) 🎖️ 🐍 🏠 🍎 🪟 🐧 - Dingo 的 MCP 服务端。Dingo是一款全面的数据质量评估工具。支持与 Dingo 基于规则和 LLM 的评估功能进行交互以及列出可用规则和提示词。
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### 📂 <a name="file-systems"></a>文件系统
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@@ -523,6 +523,7 @@ Integrations and tools designed to simplify data exploration, analysis and enhan
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- [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.
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- [arrismo/kaggle-mcp](https://github.com/arrismo/kaggle-mcp) 🐍 ☁️ - Connects to Kaggle, ability to download and analyze datasets.
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- [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.
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- [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.
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- [mckinsey/vizro-mcp](https://github.com/mckinsey/vizro/tree/main/vizro-mcp) 🎖️ 🐍 🏠 - Tools and templates to create validated and maintainable data charts and dashboards.
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- [growthbook/growthbook-mcp](https://github.com/growthbook/growthbook-mcp) 🎖️ 📇 🏠 🪟 🐧 🍎 — Tools for creating and interacting with GrowthBook feature flags and experiments.
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- [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.
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