"File system requirements: mounted directory, persistent state, and full R/W access" matching MCP servers:

An MCP server that provides an LLM with access to R's statistical toolkit including hypothesis tests, regression, psychometrics, survival analysis, time series, and plots through a persistent R session.

Enables AI assistants to execute R code, create visualizations, analyze data, and manage packages through a local Rscript CLI.

Enables users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

Enables Codex to control a Gephi 3D network view through natural language, including network generation, 3D layout, node focus and neighbor highlighting, layered views, and PNG/PDF export.

A review-only MCP server that provides tools for reviewing trade records against a live TradingView Desktop chart over Chrome DevTools Protocol on localhost.

Enables querying W\&B Weave traces and creating W\&B Reports with text and visualizations through natural language.

Options analytics an agent can drive: import a permitted chain, solve implied volatility, build a sixteen-Greek ladder, map dealer gamma walls, rank twenty-three structures, run a GARCH-t simulation with a convergence gate, and backtest with modelled premiums. Twelve stdio tools; every output is a schema-validated JSON file with source, assumptions and a degraded flag. Research only, no orders.

Enables LLMs to discover, understand, and generate Teal R Shiny applications for clinical trial data analysis.

Provides AI agents with read-only access to live chart context, candles, drawings, indicators, and provider status from the MyLinedChart desktop app, all running locally.

Enables exploratory data analysis through an MCP server, allowing AI assistants to load datasets, compute summary statistics, generate diagnostic plots, perform correlation analysis, and produce full markdown reports.

Renders OHLCV data as candlestick chart PNGs and returns inline images. It is a pure renderer with no network access, allowing models to view charts generated from provided data.

MCP server that lets agents create and update persistent concept maps via the NodeLand API, returning shareable URLs for humans to view, embed, and keep editing.

Enables any MCP client to evaluate Wolfram Language code in a persistent local Mathematica or Wolfram Engine kernel, export results to common file formats, and inspect or manage kernel status without cloud APIs, API keys, or runtime dependencies.

An MCP server that enables AI agents to compile, refine, and interlink knowledge into a persistent wiki, replacing RAG with structured, curated knowledge.

AI-powered Fantasy Premier League assistant — scored captain picks, transfer suggestions, differentials, fixture outlook, price predictions, live points, and a full manager hub that auto-detects your squad, bank balance, and free transfers.

Wraps a climate data-processing pipeline so agents can inspect the config schema and available sample CSVs, validate an inline JSON config, and run the pipeline to return a monthly summary table plus a rendered plot, with file access sandboxed to the data and outputs directories.

Data-visualization MCP server for ROOT Workspace. It enables creating and iterating on charts (Vega-Lite, Python plots) with a persistent kernel and vision loop.

Render hierarchical system diagrams as PNGs from YAML definitions using a four-level ontology of networks, factories, machines, and nodes.

Educational MCP server with 17 tools for mathematical calculations, matrix operations, statistical analysis, and data visualization. Includes persistent workspace, cloud hosting, and safety features like restricted eval.

Gives terminal coding agents a local browser canvas for rendering charts, tables, metrics, markdown, live widgets and sandboxed MCP App UIs that would otherwise reach them as raw JSON. Lets agents pose blocking visual multiple-choice questions and read back the user's clicks, selections and an animated architecture map of file activity.