"An MCP server for reading, writing, and formatting Excel workbooks locally" matching MCP servers:

Enables MCP clients to create and manipulate Excel workbooks programmatically, including sheets, cell data, formatting, formulas, charts, and pivot tables.

Enables LLMs to work with Excel and CSV files through structured tools for workbook operations, formatting, charts, ETL, analysis, and more.

Enables manipulation of Excel files including creating, reading, writing data, formatting, charts, pivot tables, and worksheet management via natural language.

A Model Context Protocol (MCP) server that lets you manipulate Excel files without needing Microsoft Excel installed. Create, read, and modify Excel workbooks with your AI agent.

Gives Claude deep tool-level control over Microsoft Excel files (.xlsx, .xlsm, .csv, .tsv) with 164 tools across 24 modules for reading, writing, formatting, formulas, charts, data analysis, and more.

Enables AI assistants to read, write, format, and analyze Excel files with 34 tools, including real-time editing on macOS with Microsoft Excel.

Publishes a spending-summary tool alongside an interactive HTML view, letting people click a category in the answer to fetch the individual expense records behind that total. Keeps calculations and their supporting evidence together in a single inspectable flow.

An MCP server that enables creating, reading, and modifying Excel workbooks, including formulas, formatting, charts, and pivot tables, without requiring Microsoft Excel.

Enables AI assistants to perform Excel file operations such as reading, writing, formatting, and chart creation through the Model Context Protocol.

Enables users to create, inspect, analyze, format, and chart Excel workbooks by having the client LLM write Python that runs in a fresh, network-restricted Docker container. Exposes a single execute_python tool with access to openpyxl, pandas, numpy, and matplotlib, persisting files between calls.

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

Enables AI coding assistants to automate live desktop spreadsheet sessions with real-time visual feedback and to create, transform, and extract .xlsx files headlessly. It supports formulas, formatting, charts, PDF export, data analysis, and Markdown/JSON interchange.

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.

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

Enables Excel automation via natural language, providing session-based workbook management, data manipulation, formulas, charts, and formatting through the Model Context Protocol.

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.

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 your own ChatGPT to build and manage a collaborative work board—dashboards, categories, datasets, monitors, and policies—through typed WebMCP tools, with human approval, audit trails, and guardrails enforced on every action.

An optimized Excel MCP server with 25+ tools for creating, reading, writing, formatting, and analyzing Excel files, including pivot tables and charts, without requiring Microsoft Excel.

Enables Claude to read a project's FastStats metrics and then design, validate against real data, and publish charts, dashboards, event markers, and custom data sources. Every write is previewed and dry-run first, with read-only mode and confirmation guards for destructive actions.

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.

MCP server providing data analytics tools for AI agents to load, clean, analyze data, generate charts and dashboards.

MCP server that wraps Nightjar's analytics API, enabling Claude Code to query events, funnels, dashboards, and metrics through natural language.

Google Search Console for Claude and other MCP clients. Sign in with Google in one click, then ask about clicks, queries, rankings and indexing, export every GSC report as CSV (beyond the 1,000-row UI limit), inspect URLs, submit sitemaps and verify sites. Includes a local SEO dashboard. Free, open source, runs on your computer.

MCP server for semantic spreadsheet operations that lets LLMs create and edit Excel workbooks by describing spreadsheet intent.

Enables AI agents to perform variance analysis, generate reconciling driver bridges and three-statement forecasts, export Excel workbooks, and analyze SEC or private-company financial data locally with engine-verified calculations.

Enables AI assistants to read and audit Tableau .twbx workbooks locally, providing tools for dependency tracing, field listing, SQL queries, filters, and semantic diffs.

Provides a local stdio MCP interface for Apache ECharts infrastructure, enabling language models and agents to access chart-related tools and resources.

Enables reading and writing spreadsheet ranges without destroying formulas, plus reshaping, formatting, and validating data within a spreadsheet.

Enables AI agents to query Google Trends for search-interest signals, returning stable JSON envelopes with calibrated searches/day estimates, relative comparisons across keywords, related and rising queries, regional interest, and country-level Trending Now data. It also surfaces explicit status and error codes so agents can distinguish partial or failed results from real numbers.