🔌 KitchenSink4XL

Landing page · llms.txt (machine-readable capability manifest for agents and LLM crawlers)

Everything plus the kitchen sink for Microsoft Excel: the .xlsx MCP server that never hands your AI a number it cannot back up. 129 workbook operations across 67 tools, a lite core that opens at about 13,800 tokens, and a safety core that backs up before every change and verifies every save.

The number that looks right and isn't

Here is the dirty secret of every tool that reads Excel files: a spreadsheet remembers its answers, not its thinking. When something changes upstream, the old answers just sit there, looking exactly like real numbers. Most AI tools will read one anyway and confidently report it, and you will make a decision based on it. This server refuses to bluff. Every number it hands your AI comes with the truth attached: freshly calculated, remembered from the last save, or missing, with the affected cells named. And when it really matters, it asks Excel itself to recalculate and prove it.

What happens to column F when you insert column D

Insert one column in a real workbook and you find out how much of a spreadsheet lives to the right of where you clicked. Formulas, totals, charts, that table on sheet three that quietly feeds the summary: all of them have opinions about where their cells just went.

Most tools do the cheap version: move the cells, save the file, hope. The damage never shows up when the edit runs. It shows up three days later, in a meeting, in the one total that quietly stopped including its last row.

This server moves everything that should move, and checks the result against what Excel itself would have done. When an edit cannot be done safely, it says no out loud instead of guessing quietly. A refusal costs you a minute. A quiet guess costs you the deal.

Two numbers that matter

• 129 workbook operations, 67 tools. Many tools here are action multiplexers, so the tool count undersells the surface: manage_worksheet alone performs seven distinct operations, manage_table nine, validate runs nine correctness batteries. The operations figure comes from scripts/count_operations.py, which reads the dispatch values each tool actually validates, out of committed source, and never from hand-math. Its docstring carries the counting definition and what is deliberately excluded. A committed snapshot (scripts/operations_snapshot.json) plus a guard test make a drifting figure a test failure rather than a marketing decision.
• Tiered loading: starts at about 13.8k tokens, scales to everything. A fresh session loads the 40-tool lite core and turns on capability packs only when a task needs them, with one enable_tools call. Load every pack and the full surface measures about 22,900 tokens. All figures come from scripts/measure_surface.py; see Context cost.

The packs

The stock is arranged in capability packs, the way a good shop groups what belongs together. A session opens on the lite core and switches on the pack a job needs with one call. Numbers below come straight from scripts/measure_surface.py, never hand-counted.

Quickstart: start lite, enable what you need

A session begins with the lite core. When a task needs more, the agent turns on the pack by name:

enable_tools(["design"])        # styles, conditional formatting, charts, names
enable_tools(["com"])           # real recalculation, real pivots, PDF export

The lite core carries no degraded stand-ins, so the lazy path is a dead end on purpose: a refusal for out-of-scope work names the exact pack and the exact call that unlocks it. Power users who want everything loaded from the start can pin it with KS4XL_MODE=full in the server environment, or a comma-separated pack list. Administrators can lock the selection with KS4XL_PACK_POLICY=locked.

Against the rest of the aisle

Feature grids flatter everybody. Here it is instead as the requests an agent actually gets:

Capability survey as of 2026-09-04, compiled from public repositories, documentation, and issue trackers. n/d: not documented in the sources surveyed, which is not a claimed absence. The most popular Excel MCP server is haris-musa/excel-mcp-server, 25 tools on openpyxl, last released 2026-04-12; its own open issues #94 and #118 ask for computed values instead of formula text. openpyxl's documentation states that it does not manage dependencies such as formulae, tables and charts when rows or columns are inserted or deleted. * The rest of the aisle: negokaz/excel-mcp-server (Go, seven tools, paginated reads, live editing on Windows), sbroenne/mcp-server-excel (C#, 31 tools over 326 operations driving the real Excel application, the capability ceiling, but Windows with Excel and exclusive access to the file, and no file-based tier). Two newer servers, PSU3D0/agent-spreadsheet and logisky/logisheets-mcp, embed real calculation engines and are worth watching. Microsoft ships no Excel MCP server, and Claude for Excel is an Office add-in rather than an MCP server. † Star counts read from the GitHub API on 2026-09-04, shown for context: stars say how long a shelf has been in view, not what is on it. Corrections welcome: open an issue.

Requirements

• Python 3.12+ (developed on 3.14)
• Most of this server needs no Excel installed at all and runs on any computer. The parts that ask Excel to do the work want Windows with Excel on it, and they open their own private copy, so the workbook you have on screen is never touched.

Install

Pick the line that describes you. Most people are the first one.