read_metadataLABSCO SUMMARY
The formula engine is what makes it more than a file reader.
Most spreadsheet tools read values and lose the logic that produced them. Parsing, compiling and evaluating formulas — and tracing dependencies between cells — means you can ask which inputs a total actually rests on, which is the question that matters when a number looks wrong. The parameter-suggestion tools are the other quiet win: real workbooks have merged headers and stray title rows, and getting the read wrong silently poisons everything after it.
What it is
A Python MCP server for Excel work, built on a two-engine architecture: pandas for analysis and a Go service using excelize for the file handling. It covers thirty-one tools spanning reading, writing, verification, charts, formula evaluation and data-quality cleanup.
What you get
- Read a workbook without guessing at it — read_metadata, read_excel_metadata, excel_read_enhanced and excel_info_enhanced, with encoding detection and structure analysis
- Get the read parameters right before reading — suggest_excel_read_parameters_tool, detect_excel_file_structure_tool and create_excel_read_template_tool, which is what saves you from a header row landing in the data
- Run your own code against a sheet — run_excel_code and run_code execute pandas inside a sandbox with a module blacklist, rather than limiting you to fixed operations
- Write back — excel_write_enhanced with formatting and styles, and batch_process_excel_files for a folder at a time
- Interactive charts as HTML — bar_chart_to_html, pie_chart_to_html, line_chart_to_html and excel_chart_enhanced
- Check the data — verify_data_integrity, validate_data_quality, comprehensive_data_verification_tool and batch_data_verification_tool
- Clean it — enhanced_data_quality_check, clean_excel_data, convert_character_formats, extract_cell_content_advanced, extract_multi_condition_data and merge_multiple_tables
- Excel formulas as first-class objects — parse_formula, compile_workbook, execute_formula, validate_formula and analyze_dependencies, so you can find out what a cell depends on rather than reading the formula bar
- excel_performance_comparison, for when you need to know which engine to use on a large file
Requirements
Python 3.8 or newer, run from a clone of this repository in its own virtualenv, with your client launching server.py. The dependency list is large — pandas, numpy, openpyxl, matplotlib, plotly, duckdb and more — so expect a substantial environment rather than a small binary, and a self-check command ships with it. Local files only; no account and no key. Code execution runs sandboxed with a module blacklist, but it is still code execution against your machine, so treat what you paste in accordingly.
Setup effort
Build from source — git clone https://github.com/Lillard01/chatExcel-mcp.git
USE CASES
Reading a messy workbook correctly the first time
Detect the structure and take the suggested read parameters before loading anything.
An analysis that no fixed tool covers
Write the pandas yourself and run it against the sheet inside the sandbox.
Working out where a wrong total came from
Parse the formula and trace which cells it actually depends on.
Checking a file before you rely on it
Run the verification pass for duplicates, types and completeness, then clean what it finds.
Turning a sheet into something people will look at
Generate an interactive chart as HTML rather than a static image.
The same job across a folder of files
Process them in a batch and verify the results together.
31 tools
Listed in project docs. Your client may show a different list.
read_excel_metadataexcel_read_enhancedexcel_info_enhancedrun_excel_coderun_codeexcel_write_enhancedexcel_chart_enhancedexcel_performance_comparisonbatch_data_verification_toolbar_chart_to_htmlpie_chart_to_htmlline_chart_to_htmlverify_data_integrityvalidate_data_qualitycomprehensive_data_verification_toolsuggest_excel_read_parameters_tooldetect_excel_file_structure_toolcreate_excel_read_template_toolparse_formulacompile_workbookexecute_formulaanalyze_dependenciesvalidate_formulaenhanced_data_quality_checkextract_cell_content_advancedconvert_character_formatsextract_multi_condition_datamerge_multiple_tablesclean_excel_databatch_process_excel_filesBEFORE YOU INSTALL
What to know first
Tools you get
31 tools named in the project's documentation.
Requires
Python 3.8+
Where it runs
The documented client runs the server on your machine.
INSTALLATION
Connect your client
Server setup
git clone https://github.com/Lillard01/chatExcel-mcp.git
cd chatExcel-mcp
python3 -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
python scripts/health_check.pyMCP Clients
Claude Desktop
{
"mcpServers": {
"chatexcel": {
"command": "<YOUR_PATH_TO_CHAT_EXCEL_MCP>/venv/bin/python",
"args": [
"<YOUR_PATH_TO_CHAT_EXCEL_MCP>/server.py"
],
"env": {
"PYTHONPATH": "<YOUR_PATH_TO_CHAT_EXCEL_MCP>"
}
}
}
}<YOUR_PATH_TO_CHAT_EXCEL_MCP> is the full path to the chatExcel-mcp folder the clone created.