excel-engine-mcp

excel-engine-mcp

Enables real Excel desktop automation to recalculate formulas, read computed cell values, and export true-fidelity PDFs, complementing library-based workflows.

Category
Visit Server

README

excel-engine-mcp

한국어 안내 → README.ko.md

A local MCP server that drives the real Excel desktop application (COM automation) to do the things file-manipulation libraries (openpyxl etc.) fundamentally cannot: recalculate formulas, read computed cell values, and render true-fidelity PDF.

Design philosophy: this server complements library workflows instead of replacing them. Writing static cells and bulk data is faster with openpyxl — but openpyxl only stores formula strings (reading them back gives None), and no library has Excel's render engine. This MCP handles only the engine-exclusive part, keeping its tool surface tiny (5 tools).

Requirements

  • Windows 10+ with a logged-in interactive desktop session (Excel has no true headless mode)
  • Microsoft Office (Excel) installed and licensed — verified on Office 2016+ (Excel 16.0)
  • Python 3.10+ — verified on 3.12
  • Claude Code or any MCP client

Install

git clone https://github.com/Feynman520/d01-p02-excel-engine-mcp.git
cd d01-p02-excel-engine-mcp
py -3.12 -m venv .venv          # or: python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt

Register with Claude Code

Run this in the cloned folder (uses absolute paths, so it works from anywhere afterwards):

claude mcp add excel-automation --scope user -- "$PWD\.venv\Scripts\python.exe" "$PWD\server.py"

--scope user makes it available in every project. Use --scope project to limit it to one project.

Verify

$py = ".\.venv\Scripts\python.exe"; $env:PYTHONUTF8 = "1"
& $py tests\smoke_com.py      # COM recalc / computed values / PDF / zombie cleanup
& $py tests\server_tools.py   # MCP tool registration (does not launch Excel)

Tools (3 core + 2 diagnostics)

# Tool Input → Output Why engine-only
① excel_recalculate src_path, out_path → {out_path} Full dependency recalculation — libraries write formulas but cannot compute them
② excel_read_values path, sheet?, cell_range?, recalc? → {sheet,address,rows,cols,values,text} Reads computed cell values (openpyxl only has the formula strings)
③ excel_export_pdf src_path, out_path, sheet?, recalc? → {out_path} WYSIWYG PDF via Excel's render engine (print areas, fit-to-page, headers/footers)
— excel_health → {alive, excel_version} Session check (launches Excel on first call)
— excel_restart → {alive} Recovery from COM errors

Typical flow: write inputs with openpyxl → excel_recalculate to bake real values → excel_read_values to verify / excel_export_pdf for the final PDF. Originals are never modified; results are always written to out_path.

Architecture notes

  • Single STA worker thread (engine/session.py): win32com COM objects are bound to the thread that created them, while FastMCP may hop threads — so every Excel call is serialized onto one dedicated thread.
  • Lazy session: Excel starts on the first tool call, is reused across calls, and is closed when the server stops.
  • DispatchEx + early binding (gencache.EnsureDispatch): a dedicated Excel instance (never attaches to the user's open Excel), called through the type library. ⚠️ ExportAsFixedFormat fails with 1004 (0x800A03EC) under late binding due to optional argument marshalling — early binding is required.
  • Zombie prevention: after Quit(), any surviving dedicated Excel process is force-killed by PID (identified via the main window handle).
  • RPC-rejection retry: RPC_E_CALL_REJECTED right after startup is retried with backoff.
  • stdout protection: the one-time makepy cache generation output is redirected to stderr (stdout is the JSON-RPC channel).

Limitations

  • Not suitable for unattended/service sessions (needs an interactive desktop).
  • Bulk static writes are faster with libraries — that is by design.

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured