mckinsey/vizro-mcp A MCP Server Official Active
Tools and templates to create validated and maintainable data charts and dashboards.
Browse all Model Context Protocol servers with quality scores, stars, languages and maintenance activity.
Tools and templates to create validated and maintainable data charts and dashboards.
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.
An MCP server to convert almost any file or web content into Markdown
Model Context Protocol (MCP) Server for Jupyter.
Official MCP server enabling seamless orchestration of hyperparameter search and other optimization tasks with [Optuna](https://optuna.org/).
Tools for creating and interacting with GrowthBook feature flags and experiments.
Model Context Protocol for R: enables AI agents to participate in interactive live R sessions.
The ultimate math engine unifying SymPy, NumPy & Matplotlib in one powerful server. Perfect for developers & researchers needing symbolic algebra, numerical computing, and data visualization.
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.
Decision intelligence MCP server with 19 algorithms (bandits, Monte Carlo, constraint optimization, forecasting, anomaly detection, risk analysis, graph algorithms), 28 MCP tools. Install via `npx -y @oraclaw/mcp-server`.
Airtight math for agents: 3.7M-theorem search, PSLQ constant ID, OEIS, real Lean kernel checks, applicability checklists. No LLM inside, no API key.
Educational MCP server for math operations, statistics, visualization, and persistent workspaces. Built with FastMCP 2.0.
Profiles tabular data files (CSV, TSV, Parquet, Excel, JSON) for LLM agents: one-call dataset overview, per-column statistics, a data-quality audit (missing values, duplicates, mixed types, outliers), and memory-saving dtype suggestions. Pure Python (pandas); files are read locally and nothing leaves your machine. `pip install data-profiler-mcp`.
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.
Create, read, validate, and save Stella system dynamics models (.stmx files in XMILE format) for scientific simulation and modeling.