Excel Vision MCP

📊 Excel Vision MCP

Your AI reads the spreadsheet. It can't see the diagram in cell B12.

This fixes that. Other Excel MCP servers return cell values and silently drop every embedded image, so the flowchart your spec depends on never reaches the model. Excel Vision MCP returns them as native ImageContent your AI can actually look at — alongside the text, formatting, and formulas.

Installation · Tools · Configuration · How It Works · FAQ

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🤔 The Problem

Ask your assistant to review a requirements spec. Half the meaning lives in screenshots, flowcharts, and annotated diagrams pasted into cells — and every Excel MCP server hands the model text only. The answer comes back confident and incomplete, because the model never knew the pictures existed.

The same blind spot applies to the *other* signals authors leave behind:

Same file. One agent sees a table of strings; the other sees what the author actually meant.

✨ Key Features

Image Extraction — What Makes This Different

Most Excel MCP servers only read cell values. This server uses a dual extraction strategy:

1. Cell-Position Mapping (primary) — Maps each image to its exact cell location using openpyxl-image-loader
2. Archive Scanning (fallback) — Scans the xlsx ZIP archive's xl/media/ directory to catch any images missed by method 1

The result: zero images left behind, with position metadata when available.

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👀 See It In Action

A requirements spec where the workflow lives in a pasted diagram and the status lives in cell colors:

You: Review this spec and tell me which features are still in scope, and how the dispatch flow works.

read_full_content returns the sheet as text with formatting markers, then the embedded diagram as an image:

Row 1: A1: Feature [B] [HL:blue] | B1: Status [B] [HL:blue]

Row 2: A2: Vehicle dispatch | B2: Approved [HL:green] Row 3: A3: Legacy CSV export [S] | B3: Cancelled [S] Row 4: A4: Driver roll call | B4: Needs review [HL:yellow]

ℹ️ Skipped 3 hidden row(s) with content. Pass include_hidden=true to read them.

[Image 1] Sheet: Spec | Cell: B12 | Original: 1180×840px [the actual flowchart, as ImageContent]

The model can now answer both halves of the question: *"Legacy CSV export is struck through and marked cancelled, so three features remain in scope — and the flowchart in B12 shows dispatch requires roll-call confirmation before assignment."*

Without image support, the second half is unanswerable. Without formatting, the cancelled row looks identical to the active ones.

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🚀 Quick Start

Install via uvx (Recommended)

No installation needed — runs directly:

uvx excel-vision-mcp

Install via pip

pip install excel-vision-mcp

Then run:

excel-vision-mcp

Install from source

git clone https://github.com/VOYAGER-Inc/excel-vision-mcp.git

cd excel-vision-mcp uv sync uv run excel-vision-mcp

Run with Docker

docker build -t excel-vision-mcp .

docker run --rm -i -v /path/to/spreadsheets:/data excel-vision-mcp

The server speaks JSON-RPC over stdin/stdout, so it must run attached (-i). Mount the directory holding your files and reference them by their in-container path (/data/report.xlsx). The image sets EXCEL_VISION_MCP_ALLOWED_DIRS=/data, so reads and writes stay inside the mount.

As an MCP client config:

{

"mcpServers": { "excel-reader": { "command": "docker", "args": ["run", "--rm", "-i", "-v", "/path/to/spreadsheets:/data", "excel-vision-mcp"] } } }

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🔧 Configuration

Add the server to your MCP client's configuration file.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{

"mcpServers": { "excel-reader": { "command": "uvx", "args": ["excel-vision-mcp"] } } }

Cursor

Edit .cursor/mcp.json in your project root:

{

"mcpServers": { "excel-reader": { "command": "uvx", "args": ["excel-vision-mcp"] } } }

Windsurf / VS Code (Copilot)

Edit your MCP settings file:

{

"mcpServers": { "excel-reader": { "command": "uvx", "args": ["excel-vision-mcp"] } } }

Antigravity IDE

Edit ~/.gemini/config/mcp_config.json:

{

"mcpServers": { "excel-reader": { "command": "uvx", "args": ["excel-vision-mcp"] } } }

Note: After editing the config, restart your IDE/client to load the new server.

Restricting file access (optional)

By default the server can read/write any Excel file your user account can access. To sandbox it to specific directories, set EXCEL_VISION_MCP_ALLOWED_DIRS (multiple paths separated by : on macOS/Linux, ; on Windows):

{

"mcpServers": { "excel-reader": { "command": "uvx", "args": ["excel-vision-mcp"], "env": { "EXCEL_VISION_MCP_ALLOWED_DIRS": "/Users/me/Documents/spreadsheets:/Users/me/Projects/data" } } } }

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🛠️ Available Tools

list_sheets

List all sheets with dimensions, merged cell counts, and image totals. Use this first to understand a workbook's structure.

list_sheets(file_path="/path/to/file.xlsx")

Returns: Sheet names, row×column dimensions, data ranges, merged cell counts, total image count.

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read_excel_data

Read cell data from a specific sheet with pagination support.

read_excel_data(

file_path="/path/to/file.xlsx", sheet_name="Sheet1", # optional, defaults to first sheet start_row=1, # optional, 1-indexed max_rows=200, # optional, default 200 include_hidden=False # optional, read hidden rows/columns too )

Returns: Cell values organized by row with coordinate labels and markers for merged cells and formatting.

Row 1: A1: Feature [B] [HL:blue] | B1: Status [B] [HL:blue]

Row 3: A3: Legacy export [S] | B3: Cancelled [S] Row 4: A4: Report | B4: Needs review [HL:yellow] | C4: Urgent [B] [C:red]

Markers and a short legend appear only on sheets that actually use formatting, so plain sheets cost no extra context.

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extract_images

Extract all embedded images from the workbook as base64 ImageContent.

extract_images(

file_path="/path/to/file.xlsx", sheet_name="Overview", # optional, None = all sheets max_width=1024, # optional, resize limit max_height=1024 # optional, resize limit )

Returns: List of ImageContent (base64) with metadata — cell position, sheet name, original dimensions.

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read_full_content ⭐

The star tool. Reads ALL text data AND all embedded images in a single call. Ideal for comprehensive document analysis.

read_full_content(

file_path="/path/to/file.xlsx", max_rows_per_sheet=500, # optional max_image_width=1024, # optional max_image_height=1024 # optional )

Returns: Complete workbook contents — every sheet's data as structured text (with formatting markers), followed by every embedded image with cell-position mapping.

Example use case: _"Analyze this requirements document and summarize all use cases, including the workflow diagrams."_

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Hidden rows & columns

All read tools skip hidden rows and columns by default — an author who hid them signalled they aren't part of the content to review.

One exception: a hidden cell that a visible formula references is still returned, marked [HIDDEN-REF], because its value drives results you can see. Skipped content is always reported so nothing disappears silently:

ℹ️ Skipped 2 hidden row(s) with content. 1 hidden cell(s) are shown anyway

because visible formulas reference them. Pass include_hidden=true to read them.

Pass include_hidden=true to read_excel_data, read_full_content, or search_excel to read everything.

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get_workbook_overview

Quick structural summary of a workbook — file size, sheet list, dimensions, image count.

get_workbook_overview(file_path="/path/to/file.xlsx")

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search_excel

Case-insensitive text search across all cells in the workbook.

search_excel(

file_path="/path/to/file.xlsx", query="revenue", sheet_name="Q4 Report" # optional, None = all sheets )

Returns: Matching cells with sheet name, coordinate, and value. Limited to 100 results.

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create_excel_file

Create a new empty workbook with the sheets you name.

create_excel_file(

file_path="/path/to/new.xlsx", sheet_names=["Summary", "Detail"], # optional, default ["Sheet1"] overwrite=False # optional, refuses to replace by default )

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add_excel_sheet

Add a new empty sheet to an existing workbook.

add_excel_sheet(file_path="/path/to/file.xlsx", sheet_name="Q3", position=0)

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update_excel_cells

Set individual cells by coordinate. Values starting with = are written as formulas.

update_excel_cells(

file_path="/path/to/file.xlsx", updates={"A1": "Title", "B2": 42, "C2": "=SUM(B2:B10)"}, sheet_name="Data" # optional, defaults to first sheet )

Note: newly written formulas show no calculated value until the file is opened in Excel. For merged ranges, write to the top-left anchor cell.

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write_excel_rows

Write a rectangular block of tabular data in one call.

write_excel_rows(

file_path="/path/to/file.xlsx", rows=[["Item", "Qty"], ["Widget", 4], ["Gad