From 4955be3627df9abe8b1ed691cf3726580c5ea426 Mon Sep 17 00:00:00 2001 From: jessicarumbelow Date: Wed, 8 Apr 2026 11:12:27 -0700 Subject: [PATCH 1/2] =?UTF-8?q?Add=20Disco=20=E2=80=94=20automated=20scien?= =?UTF-8?q?tific=20discovery=20from=20tabular=20data?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 73e5200c..7196a40c 100644 --- a/README.md +++ b/README.md @@ -1021,6 +1021,7 @@ Integrations and tools designed to simplify data exploration, analysis and enhan - [Daichi-Kudo/llm-advisor-mcp](https://github.com/Daichi-Kudo/llm-advisor-mcp) [![Daichi-Kudo/llm-advisor-mcp MCP server](https://glama.ai/mcp/servers/Daichi-Kudo/llm-advisor-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Daichi-Kudo/llm-advisor-mcp) 📇 ☁️ 🍎 🪟 🐧 - Real-time LLM/VLM model comparison with benchmarks, pricing, and personalized recommendations from 5 data sources. No API key required. - [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. +- [Disco](https://github.com/leap-laboratories/discovery-engine) [![smithery badge](https://smithery.ai/badge/discovery-engine)](https://smithery.ai/server/discovery-engine) 🐍 ☁️ - 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. - [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) [![WaveGuard MCP server](https://glama.ai/mcp/servers/WaveGuard/badges/score.svg)](https://glama.ai/mcp/servers/WaveGuard) 🐍 ☁️ 🍎 🪟 🐧 - Physics-based anomaly detection via MCP. Uses Klein-Gordon wave equations on GPU to detect anomalies with high precision (avg 0.90). 9 tools: scan, fingerprint, compare, token risk, wallet profiling, volume check, price manipulation detection. - [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. From ea57d2b251d5ced5637624fa70623974d9f4d6bb Mon Sep 17 00:00:00 2001 From: jessicarumbelow Date: Mon, 13 Apr 2026 10:35:44 -0700 Subject: [PATCH 2/2] Fix entry name to owner/repo format, add Glama badge, fix sort order Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7196a40c..cc8eb280 100644 --- a/README.md +++ b/README.md @@ -1021,12 +1021,12 @@ Integrations and tools designed to simplify data exploration, analysis and enhan - [Daichi-Kudo/llm-advisor-mcp](https://github.com/Daichi-Kudo/llm-advisor-mcp) [![Daichi-Kudo/llm-advisor-mcp MCP server](https://glama.ai/mcp/servers/Daichi-Kudo/llm-advisor-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Daichi-Kudo/llm-advisor-mcp) 📇 ☁️ 🍎 🪟 🐧 - Real-time LLM/VLM model comparison with benchmarks, pricing, and personalized recommendations from 5 data sources. No API key required. - [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. -- [Disco](https://github.com/leap-laboratories/discovery-engine) [![smithery badge](https://smithery.ai/badge/discovery-engine)](https://smithery.ai/server/discovery-engine) 🐍 ☁️ - 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. - [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) [![WaveGuard MCP server](https://glama.ai/mcp/servers/WaveGuard/badges/score.svg)](https://glama.ai/mcp/servers/WaveGuard) 🐍 ☁️ 🍎 🪟 🐧 - Physics-based anomaly detection via MCP. Uses Klein-Gordon wave equations on GPU to detect anomalies with high precision (avg 0.90). 9 tools: scan, fingerprint, compare, token risk, wallet profiling, volume check, price manipulation detection. - [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. +- [leap-laboratories/discovery-engine](https://github.com/leap-laboratories/discovery-engine) [![leap-laboratories/discovery-engine MCP server](https://glama.ai/mcp/servers/leap-laboratories/discovery-engine/badges/score.svg)](https://glama.ai/mcp/servers/leap-laboratories/discovery-engine) [![smithery badge](https://smithery.ai/badge/discovery-engine)](https://smithery.ai/server/discovery-engine) 🐍 ☁️ - 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. - [mckinsey/vizro-mcp](https://github.com/mckinsey/vizro/tree/main/vizro-mcp) 🎖️ 🐍 🏠 - Tools and templates to create validated and maintainable data charts and dashboards. - [optuna/optuna-mcp](https://github.com/optuna/optuna-mcp) 🎖️ 🐍 🏠 🐧 🍎 - Official MCP server enabling seamless orchestration of hyperparameter search and other optimization tasks with [Optuna](https://optuna.org/). - [Whatsonyourmind/oraclaw](https://github.com/Whatsonyourmind/oraclaw) [![Whatsonyourmind/oraclaw MCP server](https://glama.ai/mcp/servers/Whatsonyourmind/oraclaw/badges/score.svg)](https://glama.ai/mcp/servers/Whatsonyourmind/oraclaw) 📇 ☁️ 🏠 🍎 🪟 🐧 - Decision intelligence MCP server with 19 algorithms (bandits, Monte Carlo, constraint optimization, forecasting, anomaly detection, risk analysis, graph algorithms), 12 MCP tools. Install via `npx -y @oraclaw/mcp-server`.