"Finding the best servers for quant trading, options pricing in Python, R, Rust and market data" matching MCP servers:

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

A production-grade MCP server that provides tools to fetch historical stock data, company information, and technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands) via yfinance, and includes a Flask web dashboard for visual analysis.

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 natural language quantitative analysis for US and Indian equities, including trade plans, portfolio optimization, backtests, and price alerts using real market data.

Enables AI agents to log trades from chart screenshots, validate them against custom strategy rules, import statements from MT5/TradingView and other brokers, and review R-based statistics in a local dashboard.

Enables secure execution of statistical Python code in isolated micro-VM sandboxes, generating charts and analytical summaries.

A Python MCP server for Metabase that enables AI assistants to query databases, manage dashboards, and interact with your Metabase instance.

Enables AI agents to access Indonesian Stock Exchange market data, including Level 2 orderbooks, bandarmology, financials, chart pattern recognition, and quantitative risk budgeting.

A safety-gated Python MCP server for Metabase that lets MCP clients and agents inspect Metabase, run governed queries, and create or maintain dashboards, cards/questions, collections, snippets, permissions, and other Metabase assets through the Metabase REST API.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

Provides 102 tools for end-to-end data science pipelines, enabling data loading, cleaning, visualization, modeling, and reporting via natural language.

Enables searching and analyzing over 3,400 datasets from the Serbian government open data portal, with built-in charting and data transformation tools.

A data visualization MCP server built on FastMCP, providing 32 tools for chart generation, report building, and data processing.

Provides football analytics tools including player scouting, comparisons, market-value filters, expected-goals tables, match-by-match form, team attacking profiles, match search, shot maps, and more for 10 leagues.

Options analytics MCP server providing 40+ tools for options chain data, position valuation, Greeks, charts, and volatility analysis.

Enables scientific data introspection and visualization of VTK datasets with format-adaptive metadata extraction and interactive 3D visualization through MCP tools.

Enables data analysis and visualization operations such as loading CSV/Excel/JSON files, computing summary statistics, generating charts, and exploring datasets via SSE/HTTP.

A real MCP server exposing data-analyst tools (SQL querying, schema introspection, stats, charting) over a sales database, with a Gemini agent that discovers and calls those tools at runtime via the standard MCP protocol.

Enables MCP clients like Claude Desktop, Claude Code and Cursor to safely explore local CSV, Excel, Parquet and SQLite files by running guarded read-only DuckDB SQL over a sandboxed data directory. It also supports data profiling, chart generation as image content, and exporting query results, without ever allowing writes or access outside the configured folder.

A natural-language data analyst MCP server that lets users query SQLite sales datasets via MCP tools (list_tables, aggregate, time_series, run_sql) with read-only SQL safety guards, returning results through a FastAPI dashboard.

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.

Turns a local CSV or XLSX table into a full analysis report — summary statistics, duplicate and missing-value diagnostics, top category frequencies, significant pairwise correlations, a Markdown insights writeup, and an Excel workbook with native interactive charts — entirely offline with no uploads, API keys, or pandas dependency. It runs over stdio or streamable-http behind a free anonymous tier that returns an in-band quota signal rather than silently billing.

Provides aerodynamic analysis tools through MCP, enabling geometry generation, meshing, CFD solving, and visualization for 2D airfoils.

MCP server that renders bar, line, and pie charts from data using QuickChart, returning PNG file paths.

Enables data engineers and BI professionals to perform data pipeline development, quality assurance, visualization, and API integration locally with AI assistance.

Enables LLM clients to run Python code in a persistent, user-selected interpreter via MCP, with tools for data loading, summaries, regressions, diagnostics, and plotting, while keeping sessions alive between calls and isolating crashes.

A local, fully vectorized computational engine for stock market analysis that enables AI to perform factor calculation, strategy backtesting, IC analysis, and GPU-based multi-dimensional visualization using local DuckDB data.

Enables AI assistants to drive a live TradingView chart in Chrome, turning natural-language trading ideas into compiled Pine Script indicators, adding them to charts, and reading back results. It also supports chart navigation, indicator management, screenshots, and strategy tester backtest results.

An MCP server that answers natural-language questions over CSV, Excel, and SQL data by providing deterministic tools for loading, profiling, querying, cleaning, statistical analysis, visualization, and reporting. It enables LLMs to plan and interpret while all computation is done exactly through MCP tools.

Enables LLMs to generate visual summary reports from CSV datasets using MCP Resources, Tools, and Prompts, without exposing raw data to the model.