"A tool for working with spreadsheets and data analysis" matching MCP servers:

Provides comprehensive control over Google Sheets to read, write, format, and manage spreadsheets directly through natural language. It includes extensive tools for data manipulation, conditional formatting, and cell protection, along with integrated PostgreSQL database query capabilities.

Provides comprehensive data analysis utilities including statistical functions, probability distributions, and data processing tools through natural language.

Enables LLMs to read and write local user data, generate fake users via sampling, and interact with structured prompts and resources.

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

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

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 to search, analyze, and export Douyin (TikTok China) video and user data, including interaction metrics, content length, and keyword trends.

Enables natural-language data analysis by loading and inspecting datasets in CSV, JSON, Excel, and Parquet formats, with session management and secure file handling.

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 local-first MCP server that provides safe, governed read-only SQL access to SQLite and PostgreSQL, with validation, limits, task tracking, and report generation.

MCP server for alive-analysis. Query, search, and retrieve structured analysis history written with the ALIVE loop (Ask→Look→Investigate→Voice→Evolve), plus dashboard JSON export.

An MCP proxy that sits in front of any MCP server and exposes only the tools you actually use, ranked by usage across sessions with pins and hysteresis, while keeping every other tool reachable through one-hop search and direct calls. This keeps context small without losing access to the full upstream catalog.

Enables AI agents to search and discover data across SQLite and CSV sources through an MCP interface, with metadata indexing and fuzzy search capabilities.

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.

MCP server adapter that exposes A-share stock data tools, prompts, and resources via FastMCP, enabling querying of stocks, K-lines, financials, sectors, and market hot spots through natural language.

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.

Provides local persistent working memory for AI agents, allowing them to load rules, preferences, and project state before tasks and save validated experiences after successful completion. Works with stdio MCP clients via a local JSON file.

Prevents context bloat and the 'Lost in the Middle' phenomenon by distilling action histories into concise summaries while maintaining a clear core objective.