Data Science Tools MCP Servers

The ultimate math engine unifying SymPy, NumPy & Matplotlib in one powerful server. Perfect for developers & researchers needing symbolic algebra, numerical computing, and data visualization.

Connects to Kaggle, ability to download and analyze datasets.

A comprehensive Go-based MCP server for mathematical computations, implementing 13 mathematical tools across basic arithmetic, advanced functions, statistical analysis, unit conversions, and financial calculations.

Create, read, validate, and save Stella system dynamics models (.stmx files in XMILE format) for scientific simulation and modeling.

Deterministic local text analysis: sentiment, readability scoring, keyword extraction, text similarity, summarization, and language detection across 18 languages. Pure Python, zero heavy dependencies, 42 KB wheel. Install: pip install blackmount-nlp-mcp.

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.

Predict anything with Chronulus AI forecasting and prediction agents.

Educational MCP server for math operations, statistics, visualization, and persistent workspaces. Built with FastMCP 2.0.

Real-time LLM/VLM model comparison with benchmarks, pricing, and personalized recommendations from 5 data sources. No API key required.

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.

Tools for creating and interacting with GrowthBook feature flags and experiments.

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.

Create, manage, and automate Label Studio projects, tasks, and predictions for data labeling workflows.

connects Jupyter Notebook to Claude AI, allowing Claude to directly interact with and control Jupyter Notebooks.

Link multiple data sources (SQL, CSV, Parquet, etc.) and ask AI to analyze the data for insights and visualizations.

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.

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 gnomon-mcp.

Tools and templates to create validated and maintainable data charts and dashboards.

Official MCP server enabling seamless orchestration of hyperparameter search and other optimization tasks with Optuna.

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.

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.

Model Context Protocol for R: enables AI agents to participate in interactive live R sessions.

AI-powered code refactor engine with 80+ MCP tools for code analysis, hotspot detection, complexity metrics, persistent memory, and automated refactoring plans.

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.

Enables autonomous data exploration on .csv-based datasets, providing intelligent insights with minimal effort.

Data compression MCP server. 7 tools for gzip, brotli, deflate, and TurboQuant quantization. Auto-selects best algorithm. 60x compression on docs. Zero dependencies.

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.

Structural observability for AI conversations. Detects loops, stuck states, breakthroughs, and convergence across 17 channels without analyzing content.

Enables agents to query local information about dependencies in a Ruby project's Gemfile.

An MCP server to convert almost any file or web content into Markdown