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.
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/).
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.
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.
Connects to Kaggle, ability to download and analyze datasets.
Enables autonomous data exploration on .csv-based datasets, providing intelligent insights with minimal effort.
Link multiple data sources (SQL, CSV, Parquet, etc.) and ask AI to analyze the data for insights and visualizations.
Create, read, validate, and save Stella system dynamics models (.stmx files in XMILE format) for scientific simulation and modeling.
Compares approximate filter data structures (Bloom, Counting Bloom, Cuckoo, SuRF) via MCP
Deterministic batch tools so LLM agents stop next-token-guessing dates and math. Rich `now()` snapshot (18 fields), `calendar(ops)` batch dispatcher (diff/until/since/add/weekday/business_days, natural-language parsing), `calc(expressions)` Python eval with math+stats pre-loaded, and Pint-based unit conversion. One wiring for dates + math + units. Listed in the official MCP Server Registry. `uvx g
Unit conversion and dimensional analysis backed by the bundled GNU units database (3000+ units, compound expressions, reduction to SI base units). Offline and deterministic. `uvx mcp-gnu-units`.
Create, manage, and automate Label Studio projects, tasks, and predictions for data labeling workflows.
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.
Predict anything with Chronulus AI forecasting and prediction agents.
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.
LLM quantization via tool call. Convert models to GGUF, GPTQ, and AWQ formats. Recommend optimal quant settings, evaluate quality, and push to Hugging Face Hub.
This Kaggle MCP Server makes Kaggle more accessible by letting you browse competitions, leaderboards, models, datasets, and kernels directly within MCP, streamlining discovery for data scientists and developers.
Structural observability for AI conversations. Detects loops, stuck states, breakthroughs, and convergence across 17 channels without analyzing content.