Excel MCP Server
The Excel MCP Server provides Claude with comprehensive read, write, and analysis capabilities for Excel and CSV files through 37 specialized tools.
Read & Navigate: Read entire files or specific cells/ranges, get headers, search values, filter rows by conditions, and aggregate columns (sum, average, count, min, max).
Large File Handling: Stream large files (100MB+) in chunks and get file analysis with chunking recommendations.
Write & Format: Create new Excel/CSV files (single or multi-sheet with formulas and inter-sheet references), add sheets to existing files, export analysis results, apply cell formatting (fonts, colors, borders, alignment, number formats), and auto-fit columns.
Analytics: Perform statistical analysis, correlation analysis, data profiling, and create pivot tables.
Financial Modeling: Conduct DCF valuation, budget vs. actual variance analysis, financial ratio analysis with industry benchmarks, scenario/what-if modeling, and trend analysis with forecasting.
Data Cleaning: Find/manage duplicates, batch clean data (trim whitespace, fix dates/phones/names/currency, remove empty rows), and perform intelligent VLOOKUP with fuzzy matching and error handling.
Bulk Operations: Aggregate and filter data across multiple files in parallel.
Validation: Cross-validate data integrity across related files (referential integrity, data completeness, value ranges).
AI-Powered: Evaluate Excel formulas, convert natural language to formulas/commands, explain formulas in plain English, get AI-powered analysis suggestions, and check AI provider status (Anthropic, OpenAI, DeepSeek, Gemini, or local fallback).
Enables AI-powered natural language data queries and formula generation through OpenAI's API for intelligent spreadsheet analysis.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Excel MCP Server sum all sales where region is 'North'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Excel MCP Server
MCP server that gives Claude full read/write/analyze power over Excel and CSV files. 37 tools — from basic cell reads to financial modeling.
Install
Option 1: npm (Recommended)
npm install -g excel-csv-mcp-serverThen add to your MCP client:
Claude Code:
claude mcp remove excel-csv # if previously added
claude mcp add excel-csv --transport stdio excel-csv-mcp-serverClaude Desktop / Cursor — add to your MCP config (claude_desktop_config.json or Cursor's mcp.json):
{
"mcpServers": {
"excel-csv": {
"command": "excel-csv-mcp-server"
}
}
}Option 2: npx (No Install)
No global install needed — runs directly:
Claude Code:
claude mcp add excel-csv stdio npx -- excel-csv-mcp-serverClaude Desktop / Cursor:
{
"mcpServers": {
"excel-csv": {
"command": "npx",
"args": ["-y", "excel-csv-mcp-server"]
}
}
}Option 3: From Source
git clone https://github.com/ishayoyo/excel-mcp.git
cd excel-mcp
npm install
npm run buildClaude Code:
claude mcp add excel-csv stdio node /path/to/excel-mcp/dist/index.jsClaude Desktop / Cursor:
{
"mcpServers": {
"excel-csv": {
"command": "node",
"args": ["/path/to/excel-mcp/dist/index.js"]
}
}
}Related MCP server: mcp-data-lens
What It Can Do
Category | Tools | Examples |
Read & Navigate |
| Read files, search values, filter rows, sum columns |
Large Files |
| Stream 100MB+ files in chunks |
Write & Format |
| Create Excel/CSV, multi-sheet with formulas, style cells |
Analytics |
| Stats, correlations, profiling, pivot tables |
Financial |
| DCF valuation, budget vs actual, financial ratios, what-if scenarios |
Data Cleaning |
| Remove duplicates, fix dates/phones/names, cross-file lookups |
Bulk Ops |
| Aggregate/filter across multiple files |
Validation |
| Cross-file referential integrity checks |
AI-Powered |
| Evaluate formulas, natural language to formula, AI analysis |
AI Providers (Optional)
For AI-powered tools (parse_natural_language, explain_formula, smart_data_analysis), create a .env file:
cp .env.example .envANTHROPIC_API_KEY=your-key
OPENAI_API_KEY=your-key
DEEPSEEK_API_KEY=your-key
GEMINI_API_KEY=your-keyAny single provider is enough. A local fallback works without any keys.
Data leaves your machine when a provider key is set. The AI tools send the spreadsheet content they are analysing to whichever provider you configured (Anthropic, OpenAI, DeepSeek, or Gemini). Leave the keys unset to keep every operation local -- the built-in fallback provider makes no network calls.
Security
The server reads and writes only .csv, .xlsx, and .xls files, and runs over
stdio as a local process with your own user's permissions.
Restricting file access
By default any path you name is allowed, because opening a spreadsheet anywhere on your disk is the point of the tool. If you expose the server to callers you do not fully trust -- or want a guardrail against a malicious spreadsheet talking the model into writing somewhere unexpected -- confine it to one directory:
EXCEL_MCP_WORKSPACE_ROOT=/path/to/your/dataEvery read and write then resolves inside that directory, and anything escaping it is rejected. Unset the variable to restore the default behaviour.
Note that containment is checked after the path is resolved, so .. traversal is
handled, but symlinks are not followed: a symlink inside the root that points
outside it will still resolve. Avoid placing untrusted symlinks in the workspace.
CSV formula injection
Spreadsheet applications evaluate a cell beginning with =, +, -, @, tab, or
carriage return as a formula. When writing CSV, this server prefixes such values
with a single quote so they stay literal text (CWE-1236). Plain numbers like -5
and +1.5 are recognised and left alone, so numeric data round-trips unchanged.
This guard is on by default. To emit raw values instead:
EXCEL_MCP_CSV_FORMULA_GUARD=offExcel output is unaffected -- .xlsx cells are written as typed strings, which are
never evaluated as formulas.
License
MIT
Available Tools
35 toolsadd_sheetC
Add a new sheet to an existing Excel file
Inputs: filePath (required), sheetName (required), data (required), headers, +1 more.
aggregateC
Perform aggregation operations on a column
Inputs: filePath (required), column (required), operation (required), sheet.
ai_provider_statusB
Check status of available AI providers
Inputs: no named parameters.
auto_fit_columnsB
Automatically adjust column widths to fit content in Excel files
Inputs: filePath (required), sheet, columns, minWidth, +2 more.
budget_variance_analysisC
Analyze budget vs actual performance with variance calculations
Inputs: filePath (required), actualColumn (required), budgetColumn (required), sheet.
bulk_aggregate_multi_filesB
Aggregate same column across multiple files in parallel
Inputs: filePaths (required), column (required), operation (required), consolidate, +2 more.
bulk_filter_multi_filesC
Filter data across multiple files with optional export
Inputs: filePaths (required), filters (required), outputMode (required), outputPath, +1 more.
correlation_analysisC
Calculate correlation between two numeric columns
Inputs: filePath (required), column1 (required), column2 (required), sheet.
data_cleanerC
Batch data cleaning operations with intelligent detection of common data quality issues
Inputs: filePath (required), operations, preview, sheet.
data_profileC
Generate comprehensive data profiling report for all columns
Inputs: filePath (required), sheet.
dcf_analysisC
Perform Discounted Cash Flow (DCF) valuation analysis for investment evaluation
Inputs: filePath (required), sheet, assumptions.
evaluate_formulaC
Evaluate an Excel formula with given context
Inputs: formula (required), context.
explain_formulaB
Explain what an Excel formula does in plain English
Inputs: formula (required), provider.
export_analysisC
Export analysis results (pivot tables, statistics, etc.) to a new file
Inputs: analysisType (required), sourceFile (required), outputFile (required), analysisParams (required).
filter_rowsC
Filter rows based on column values
Inputs: filePath (required), column (required), condition (required), value (required), +1 more.
find_duplicatesC
Find and manage duplicate rows in Excel/CSV files with multiple strategies
Inputs: filePath (required), columns, action, keepFirst, +1 more.
format_cellsC
Apply formatting to Excel cells (fonts, colors, borders, alignment)
Inputs: filePath (required), range (required), styling (required), sheet.
get_cellC
Get the value of a specific cell using A1 notation
Inputs: filePath (required), cell (required), sheet.
get_file_infoC
Analyze file size and get chunking recommendations
Inputs: filePath (required), sheet.
get_headersB
Get the column headers (first row) of a file
Inputs: filePath (required), sheet.
get_rangeC
Get values from a range of cells
Inputs: filePath (required), startCell (required), endCell (required), sheet.
parse_natural_languageC
Convert natural language to Excel formula or command
Inputs: query (required), filePath, provider.
pivot_tableC
Create pivot table with grouping and aggregation
Inputs: filePath (required), groupBy (required), aggregateColumn (required), operation (required), +1 more.
ratio_analysisC
Perform comprehensive financial ratio analysis with industry benchmarks
Inputs: filePath (required), sheet.
read_fileB
Read an entire CSV or Excel file with optional chunking for large files
Inputs: filePath (required), sheet, offset, limit.
read_file_chunkedB
Read large files in manageable chunks to avoid token limits
Inputs: filePath (required), sheet, chunkIndex, chunkSize.
scenario_modelingC
Perform what-if scenario analysis with multiple assumptions
Inputs: filePath (required), scenarios (required), sheet.
searchC
Search for cells containing a specific value
Inputs: filePath (required), searchValue (required), exact, sheet.
smart_data_analysisC
AI-powered analysis suggestions for your data
Inputs: filePath (required), sheet, provider.
statistical_analysisC
Perform comprehensive statistical analysis on a column
Inputs: filePath (required), column (required), sheet.
trend_analysisC
Analyze time series trends, growth rates, seasonality, and forecasting for sales and performance data
Inputs: filePath (required), dateColumn (required), valueColumn (required), sheet, +1 more.
validate_data_consistencyC
Cross-validate data integrity across related files
Inputs: primaryFile (required), referenceFiles (required), validationRules, keyColumns, +4 more.
vlookup_helperB
Intelligent VLOOKUP setup and execution with error handling and fuzzy matching
Inputs: sourceFile (required), lookupFile (required), lookupColumn (required), returnColumns, +4 more.
write_fileC
Write data to a new CSV or Excel file (supports multiple sheets for Excel)
Inputs: filePath (required), data, headers, sheet, +1 more.
write_multi_sheetC
Create a complex Excel file with multiple sheets, formulas, and inter-sheet references
Inputs: filePath (required), sheets (required), sheetReferences.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 35 tools
The tools cover distinct Excel/data analysis functions, but there is significant overlap in some areas. For example, 'read_file' and 'read_file_chunked' serve similar purposes with minor differences, and 'bulk_aggregate_multi_files' and 'aggregate' could be confused for related operations. Descriptions help clarify, but the high count increases potential for misselection.
Naming conventions are mixed, with some tools using verb_noun patterns (e.g., 'add_sheet', 'filter_rows') and others using noun_verb or descriptive phrases (e.g., 'data_cleaner', 'dcf_analysis'). While readable, the lack of a uniform pattern across all 35 tools reduces predictability and consistency.
With 35 tools, the server feels overloaded for an Excel-focused domain. Many tools could be consolidated or parameterized (e.g., 'read_file' and 'read_file_chunked'), leading to a heavy and potentially confusing interface. This exceeds the typical well-scoped range of 3-15 tools, indicating poor scoping.
The tool set provides extensive coverage for Excel operations, including data I/O, analysis, formatting, and advanced functions like AI integration and financial modeling. Minor gaps exist, such as limited editing capabilities for existing files, but agents can work around these given the broad functionality available.
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