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Model Context Protocol (MCP) Server for Jupyter.
Superhuman exploratory data analysis that finds the feature interactions and subgroup effects that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Data goes in, validated insights come out. Free for public data.
connects Jupyter Notebook to Claude AI, allowing Claude to directly interact with and control Jupyter Notebooks.
Agent-operable ML experiment contract (cq.yaml + JSON contracts) with a built-in MCP server exposing 14 tools (resolve/inspect/run/validate/describe/compare/lineage) for running, validating, and tracing experiments across any framework (PyTorch / HF Trainer / Lightning / sklearn / XGBoost). Apache-2.0.
Compares approximate filter data structures (Bloom, Counting Bloom, Cuckoo, SuRF) via MCP
Create, manage, and automate Label Studio projects, tasks, and predictions for data labeling workflows.
Enables agents to query local information about dependencies in a Ruby project's `Gemfile`.
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.
The first NetworkX integration for Model Context Protocol, enabling graph analysis and visualization directly in AI conversations. Supports 13 operations including centrality algorithms, community detection, PageRank, and graph visualization.
Tools and templates to create validated and maintainable data charts and dashboards.
Connects to Kaggle, ability to download and analyze datasets.
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