"A server for assisting researchers in novel discovery and document analysis" matching MCP servers:

Enables users to preprocess, analyze, and visualize CSV data through comprehensive tools for data manipulation, statistical analysis, and graph generation.

Enables AI agents to index and search across SQLite databases and CSV files to discover table schemas and column metadata. It provides a unified MCP API for data source management and structural exploration through natural language.

Enables conversion between PDF/Office documents and structured JSON, supporting extraction and generation of document content.

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.

Convert PDFs to structured JSON. Extract invoices, bank statements, contracts, and more. Pay per call via x402 USDC.

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

基于Model Context Protocol (MCP) 的A股实时行情查询服务器，支持查询A股实时价格、历史K线数据、财务信息及市场概况。

A fast, safe MCP server for YNAB providing read-only budget access by default, with 28 tools for budgets, transactions, categories, payees, accounts, scheduled transactions, and server-computed analytics.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.

Enables natural language-powered ETL workflows using Airflow, AWS Glue, Athena, and S3, allowing LLM agents to control and monitor data infrastructure.

An MCP server for data analysis and visualization supporting CSV and Excel files. It enables users to generate statistical summaries and create multi-dimensional charts like heatmaps and bar plots through natural language.

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 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.

Enables LLMs to retrieve, analyze, and visualize stock prices and financial report data for quantitative trading research and investment analysis. Provides real-time and historical stock data, financial statement analysis, key metric calculations, and trading signal visualization.

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.

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.

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 real-time stock market analysis through AKShare API integration. Supports retrieving live stock prices, historical data, technical indicators (MA, MACD, RSI), market sentiment analysis, stock search, and financial news.

Superhuman data-driven science. Allows agents to upload any tabular dataset, specify a target column, and get validated predictive patterns (with p-values, effect sizes, and context from literature) that surface feature interactions and subgroup effects you'd otherwise miss. Many discoveries already made, free for open data!