"Python Code for Data Analysis" matching MCP servers:

Provides comprehensive statistical analysis tools for industrial data including time series analysis, correlation calculations, stationarity tests, outlier detection, causal analysis, and forecasting capabilities. Enables data quality assessment and statistical modeling through a FastAPI-based MCP architecture.

Enables comprehensive analysis of CSV files and SQLite databases through tools for statistics, correlations, anomaly detection, pivot tables, time series analysis, visualization, and automated insights discovery.

Generates and deploys Cloudera Data Engineering (CDE) Airflow jobs from ODCS data contracts, validating live table data in CDW against schema and data-quality rules without WAP staging.

An MCP server for dataset exploration and analysis, enabling LLM clients to perform summary, correlation, distribution, missing value analysis, data cleaning, and statistical tests directly on CSV files.

A Python MCP server for hypothesis-driven data analysis, managing analysis designs, data catalogs, and review workflows through Claude Code or any MCP-compatible client.

An MCP server that enables advanced CSV analysis and data visualization using Google's Gemini AI and Plotly. It allows users to perform exploratory data analysis, generate interactive charts, and conduct complex reasoning on tabular data.

Enables AI clients to investigate data engineering workflows by exposing tools for job status, logs, schema lookup, read-only SQL, and searching documentation/incidents, along with resources and prompts for ETL failure analysis.

A Model Context Protocol server that enables Claude to load CSV files and execute JavaScript data analysis scripts for advanced data exploration and insights generation.

A Model Context Protocol server for data wrangling that provides standardized interfaces for data preprocessing, transformation, and analysis tasks including data aggregation and descriptive statistics.

Enables LLMs to query live data from over 250+ sources via CData Python Connectors using natural language. Wraps connectors as a Model Context Protocol server.

A FastMCP-based server that provides data analysis tools for processing, analyzing, and visualizing data with an intuitive Streamlit web interface.

Provides a read-only MCP interface to locally synchronized Amazon seller data, enabling AI agents and analytics tools to query order, inventory, advertising, and settlement information with completeness and freshness checks for safe analysis.

An MCP server that provides data bridging from Spring Boot interfaces and a lightweight Python sandbox for script execution. It enables agents to fetch data as Markdown or Parquet files and perform automated data analysis within a controlled environment.

Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

Enables fetching market and options data including OHLCV prices, option chains, Greeks, and corporate events through a pluggable FastAPI service. Supports creating aligned training datasets for financial analysis and uses yfinance by default with support for custom data providers.

Enables AI agents to compute and apply minimal JSON Patch (RFC 6902) operational diffs for atomic state synchronization between structured documents. Runs as a zero-dependency Python MCP server with deterministic outputs and sub-millisecond execution overhead.

A Python-based FastMCP server that provides financial tools for securities analysis, including market data, news, fundamental/technical analysis, and visualization capabilities that can be consumed by any MCP-aware client.

Enables autonomous agents to compute and atomically apply minimal JSON Patch (RFC 6902) operational diffs for structured data state synchronization. Runs as a zero-dependency Python MCP server supporting deterministic patching, caching, and rate-limiting workflows.

Enables conversational analysis of CSV and Parquet files through natural language, providing statistics, summaries, data type information, and comprehensive multi-step data analysis.