ExceLLM: Excel Live MCP Server

A Model Context Protocol (MCP) server for Excel automation with dual-engine support. Enables LLMs (Claude, ChatGPT, Cursor, etc.) to interact with Excel files through natural language - live on Windows or file-based cross-platform.

Features

• ✅ Dual-Engine Architecture: Live Excel (Windows COM) or file-based (cross-platform)
• ✅ 34 MCP Tools: Comprehensive Excel automation toolkit
• ✅ Real-Time Excel Operations: Work with files open in Excel (Windows)
• ✅ Cross-Platform File Mode: Work with .xlsx files on Windows, Mac, Linux
• ✅ Charts & Pivot Tables: Native Excel chart and pivot table creation
• ✅ VBA Execution: Execute custom macros (Windows only, disabled by default)
• ✅ Screen Capture: Visual verification of changes (Windows only)
• ✅ Excel Tables: Create, list, delete table objects
• ✅ Cell Merging: Merge, unmerge, and query merged cells
• ✅ Formula Validation: Validate syntax before applying
• ✅ Session Management: Process large datasets with chunking
• ✅ Advanced Search & Filtering: Find and filter data efficiently
• ✅ LLM-Optimized: Workflow guidance and structured responses

Prerequisites

For Live Excel Mode (Windows)

• Windows OS (required for COM automation)
• Microsoft Excel installed and running
• Python 3.10 or higher
• pywin32 library for Windows COM automation
• At least one workbook open in Excel

For File Mode (Cross-Platform)

• Any OS: Windows, Mac, or Linux
• Python 3.10 or higher
• openpyxl library for file operations
• No Excel required

Installation

From PyPI (Recommended)

pip install excellm

From Source

cd ExceLLM
pip install -e .

Development Installation

cd ExceLLM
pip install -e ".[dev]"

Usage

Running the Server

python -m excellm

The server will start and listen for MCP client connections using stdio transport.

MCP Client Integration

Claude Desktop

Windows Configuration:

Create or edit %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "ExceLLM": {
      "command": "python",
      "args": [
        "-m",
        "excellm"
      ],
      "description": "Real-time Excel automation for open files"
    }
  }
}

macOS Configuration:

Create or edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "ExceLLM": {
      "command": "python3",
      "args": [
        "-m",
        "excellm"
      ],
      "description": "Real-time Excel automation for open files"
    }
  }
}

Important:

• Replace python with full path if needed (e.g., C:\\Python311\\python.exe)
• Use double backslashes \\ for Windows paths
• Restart Claude Desktop after making changes

Cursor AI Editor

Open Cursor Settings → MCP and add:

{
  "mcpServers": {
    "ExceLLM": {
      "command": "python",
      "args": ["-m", "excellm"]
    }
  }
}

ChatGPT (with MCP support)

Configure in ChatGPT's MCP settings similarly to above.

Available Tools

🔍 Inspection (Start Here)

1. inspect_workbook()

Fast workbook-level radar. Returns sheet names, visibility, and "scent" of data without reading cells. Usage: await inspect_workbook()

2. explore(scope, mode="quick")

Sheet-level analysis. Detects used range, labeled regions, and layout. Usage: await explore({"sheet": "Sheet1"}, mode="deep")

---

📖 Reading & Navigation

3. list_open_workbooks()

List all open workbooks and their sheets. Usage: await list_open_workbooks()

4. read(workbook_name, sheet_name, reference, batch, max_rows)

Read cells or ranges. Supports single range OR efficient batch reading. Usage: await read("data.xlsx", "Sheet1", "A1:D10") Batch: await read("data.xlsx", batch=[{"sheet": "S1", "range": "A1"}, {"range": "B2"}]) Pagination: await read("data.xlsx", "Sheet1", max_rows=100) — limits rows to prevent token explosion

5. search(workbook_name, filters, ..., max_rows)

Filter data server-side before returning to LLM. Usage: await search("data.xlsx", {"Column": "Status", "Value": "Active"}) Pagination: await search("data.xlsx", "active", max_rows=50) — limits matching rows

6. get_unique_values(workbook_name, sheet_name, range)

Get unique values and counts from a column. Usage: await get_unique_values("data.xlsx", "Sheet1", "A:A")

7. get_current_selection()

Get the currently selected cell/range in the active window. Usage: await get_current_selection()

8. select_range(workbook_name, sheet_name, reference)

Visually select a range in the Excel UI. Usage: await select_range("data.xlsx", "Sheet1", "A1:B10")

---

🕒 History & Changes

9. get_recent_changes(limit=10)

Get the history of recent user actions from both Undo and Redo stacks. Usage: await get_recent_changes(limit=5) Returns: {"undo_history": [...], "redo_history": [...]}

---

✍️ Writing & Editing

10. write(workbook_name, sheet_name, reference, data, ...)

Write values. Supports single cells or 2D arrays. Includes safety checks. Usage: await write("data.xlsx", "Sheet1", "A1", "Hello")

11. copy_range(source_workbook, source_sheet, source_range, target_sheet, ...)

Copy data between locations, preserving formatting. Usage: await copy_range("data.xlsx", "Sheet1", "A1:A10", target_sheet="Sheet2")

12. find_replace(workbook_name, find_value, replace_value, ...)

Find and replace text. Usage: await find_replace("data.xlsx", "Old", "New")

13. sort_range(workbook_name, sheet_name, range, sort_by)

Sort data by multiple columns. Usage: await sort_range("data.xlsx", "Sheet1", "A1:D50", [{"column": "A", "order": "asc"}])

---

🎨 Formatting

14. format(workbook_name, sheet_name, reference, style, format)

Apply styles (color, bold, number format) to ranges. Usage: await format("data.xlsx", "Sheet1", "A1", style="header")

15. get_format(workbook_name, sheet_name, reference)

Read formatting properties of a range. Usage: await get_format("data.xlsx", "Sheet1", "A1")

16. merge_cells(workbook_name, sheet_name, start_cell, end_cell)

Merge a range of cells. Usage: await merge_cells("data.xlsx", "Sheet1", "A1", "D1")

17. unmerge_cells(workbook_name, sheet_name, start_cell, end_cell)

Unmerge previously merged cells. Usage: await unmerge_cells("data.xlsx", "Sheet1", "A1", "D1")

18. get_merged_cells(workbook_name, sheet_name)

List all merged cell ranges in a sheet. Usage: await get_merged_cells("data.xlsx", "Sheet1")

💡 Conditional Formatting: The format tool now supports conditional_format parameter:

• ColorScale: {type: "colorScale", min_color: "FF0000", max_color: "00FF00"}
• DataBar: {type: "dataBar", bar_color: "638EC6"}
• IconSet: {type: "iconSet", icon_style: "3trafficlights"}
• CellIs: {type: "cellIs", operator: "greaterThan", value: 100, fill_color: "FFEB9C"}

---

📋 Sheet & Structure Management

19. manage_sheet(workbook_name, action, sheet_name, ...)

Add, rename, delete, hide, copy, or move worksheets. Usage: await manage_sheet("data.xlsx", action="add", sheet_name="NewSheet")

20. insert(workbook_name, sheet_name, insert_type, position, count)

Insert rows or columns. Usage: await insert("data.xlsx", "Sheet1", "row", "5", count=2)

21. delete(workbook_name, sheet_name, delete_type, position, count)

Delete rows or columns. Usage: await delete("data.xlsx", "Sheet1", "column", "C")

---

📊 Excel Tables

22. create_table(workbook_name, sheet_name, range_ref, table_name)

Convert a range into an official Excel Table (ListObject). Usage: await create_table("data.xlsx", "Sheet1", "A1:D10", "SalesTable")

23. list_tables(workbook_name)

List all tables in the workbook. Usage: await list_tables("data.xlsx")

24. delete_table(workbook_name, sheet_name, table_name, keep_data)

Remove table structure, optionally keeping data. Usage: await delete_table("data.xlsx", "Sheet1", "SalesTable")

---

📈 Charts & Pivot Tables

25. create_chart(workbook_name, sheet_name, data_range, chart_type, target_cell, ...)

Create charts (line, bar, pie, scatter, area) from data.

• Live Mode: Native Excel chart automation.
• File Mode: Basic chart creation via openpyxl. Usage: await create_chart("data.xlsx", "Sheet1", "A1:D10", "bar", "F1", title="Sales")

26. create_pivot_table(workbook_name, sheet_name, data_range, rows, values, ...)

Create pivot tables with aggregation.

• Live Mode: Native interactive Excel pivot table.
• File Mode: Static summary table (calculated in Python). Usage: await create_pivot_table("data.xlsx", "Sheet1", "A1:D100", rows=["Category"], values=["Amount"], agg_func="sum")

---

⚙️ Advanced Features

27. execute_vba(workbook_name, vba_code)

Run custom VBA macros (Windows only).

🔒 Security: Disabled by default. Set EXCELLM_ENABLE_VBA=true to enable.

Usage: await execute_vba("data.xlsx", "Range('A1').Value = 'VBA'")

28. capture_sheet(workbook_name, sheet_name, range_ref)

Take a screenshot of a range (Windows only). Usage: await capture_sheet("data.xlsx", "Sheet1", "A1:H10")

29. validate_cell_reference(cell)

Utility to check if a reference string is valid. Usage: await validate_cell_reference("A1")

30. validate_formula(formula)

Validate Excel formula syntax, check for errors, and get correction suggestions. Usage: await validate_formula("=SUM(A1:A10)")

---

🚀 Big Data Sessions (Stateful)

For handling large datasets (>50 rows) safely.

31. create_transform_session(...)

Start a session to process data in chunks. Usage: await create_transform_session("data.xlsx", "Sheet1", "A", "B")

32. process_chunk(session_id, data)

Submit processed data for the current chunk. Usage: await process_chunk("session_123", [[1, 2], [3, 4]])

33. get_session_status(session_id)

Check progress of a session. Usage: await get_session_status("session_123")

34. create_parallel_sessions(...)

Split work for parallel sub-agents. Usage: await create_parallel_sessions("data.xlsx", "Sheet1", "A", "B")

---

Example Usage:

User: Replace all "N/A" with "Not Available" in Sheet1
LLM: [Calls find_replace("data.xlsx", "N/A", "Not Available", sheet_name="Sheet1")]
  ✓ Found 15 matches, replaced 15 values

User: How many cells contain "error"? Don't replace yet.
LLM: [Calls find_replace("data.xlsx", "error", "", preview_only=True)]
  Found 7 cells containing "error" (preview mode, no changes made)

---

Common Workflows

📊 Recommended Workflow Patterns

ExceLLM tools include workflow guidance markers (🔍, ✍️, ⚙️, 📸, 📊) to help LLMs use tools in the correct order.

---

Pattern 1: Data Analysis Workflow

Recommended Order:

1. 🔍 Inspect → Understand workbook/sheet structure
2. 📖 Read → Get data from identified regions
3. 📊 Process → Analyze data
4. ✍️ Write → Write results back
5. 📸 Verify (optional) → Visual validation

Example:

User: Analyze sales data in data.xlsx and write summary

LLM Workflow:
1. inspect_workbook() 
   → Identifies sheets, finds "Sales" sheet
   
2. explore({"sheet": "Sales"}, mode="quick")
   → Detects data in A1:D100, headers present
   
3. read("data.xlsx", "Sales", "A1:D100")
   → Reads all sales data

4. [Analysis] → Calculates totals, averages
   
5. write("data.xlsx", "Summary", "A1:C5", summary_data)
   → Writes analysis to Summary sheet
   
6. capture_sheet("data.xlsx", "Summary") [OPTIONAL]
   → Screenshots for validation

---

Pattern 2: Data Transformation (Large Datasets)

For datasets > 25 rows:

1. 🔍 Explore → Understand data structure
2. 🎯 Create Session → Start stateful processing
3. 🔄 Process Chunks → Iterate through chunks
4. ✅ Verify → Check session status

Example:

User: Extract PO numbers from 500 rows of messy text

LLM Workflow:
1. explore({"sheet": "Data"}, mode="deep")
   → Identifies 500 rows in column A
   
2. create_transform_session(
     workbook_name="data.xlsx",
     sheet_name="Data",
     source_column="A",
     output_columns="B:D",
     start_row=2,
     chunk_size=25
   )
   → Session created, first chunk received
   
3. process_chunk(session_id, transformed_data)
   → Process chunk 1 (rows 2-26)
   → Server returns next chunk automatically
   
4. process_chunk(session_id, transformed_data)
   → Process chunk 2 (rows 27-51)
   → Continue until complete
   
5. get_session_status(session_id)
   → Verify all 500 rows processed

---

Pattern 3: Formatting & Styling

Recommended Order:

1. 📖 Read/Explore → Identify target range
2. ✍️ Write → Write data if needed
3. 🎨 Format → Apply formatting
4. 📊 Create Table (optional) → Convert to Excel table
5. 📸 Capture (optional) → Visual verification

Example:

User: Create a formatted sales table with styling

LLM Workflow:
1. write("data.xlsx", "Sheet1", "A1:D100", sales_data)
   → Write raw data
   
2. format("data.xlsx", "Sheet1", "A1:D1", style="header")
   → Format header row
   
3. format("data.xlsx", "Sheet1", "B2:D100", 
          format={"numberFormat": "$#,##0.00"})
   → Currency formatting for amounts
   
4. create_table("data.xlsx", "Sheet1", "A1:D100",
                "SalesData", table_style="medium9")
   → Convert to Excel table with filters
   
5. capture_sheet("data.xlsx", "Sheet1", "A1:D100")
   → Screenshot for validation

---

Pattern 4: Advanced Operations (VBA)

⚠️ USE WITH CAUTION - Only when standard tools are insufficient

Recommended Order:

1. 🔍 Explore → Understand current state
2. ⚙️ Execute VBA → Run custom macro
3. 📸 Capture → Verify results visually

Example:

User: Apply complex conditional formatting that requires VBA

LLM Workflow:
1. explore({"sheet": "Data"})
   → Understand data layout
   
2. execute_vba("data.xlsx", '''
     Dim ws As Worksheet
     Set ws = ActiveSheet
     
     ' Complex conditional formatting
     ws.Range("A1:D100").FormatConditions.Add ...
     ws.Range("A1:D100").FormatConditions(1).Interior.Color = RGB(255,200,200)
   ''')
   → Execute VBA for complex operation
   
3. capture_sheet("data.xlsx", "Data", "A1:D100")
   → Verify formatting applied correctly

---

Workflow 1: Quick Data Update

1. List workbooks → identify target file
2. Read current cell value
3. Calculate/update value
4. Write new value back
5. Save workbook

Example:

User: Find all cells in column A that are empty in data.xlsx and fill them with "N/A"
LLM: [Chains multiple tool calls]
  1. list_open_workbooks() - find data.xlsx
  2. read_range("data.xlsx", "Sheet1", "A1:A100") - get values
  3. [Processing] - identify empty cells
  4. write_range() - write "N/A" to empty cells
  5. save_workbook("data.xlsx") - save changes
  ✓ Filled 15 empty cells with "N/A"

---

Workflow 2: Data Analysis

1. Read range of data
2. Analyze data patterns
3. Calculate statistics
4. Write summary to Excel

Example:

User: Analyze the sales data in data.xlsx and write a summary to the Summary sheet
LLM: [Chains tools]
  1. read_range("data.xlsx", "Sheet1", "A2:D100") - get sales
  2. [Analysis] - calculates total, average, max/min
  3. write_range("data.xlsx", "Summary", "A1:C5", [summary data])
  4. save_workbook("data.xlsx")
  ✓ Analysis complete. Summary written to Summary sheet.

---

Workflow 3: Cell-by-Cell Validation

1. Read a cell
2. Validate format
3. Update status in another cell

Example:

User: Validate all email addresses in column B and mark invalid ones in column C
LLM: [Iterative workflow]
  ✓ Validated 50 email addresses
  ✓ Marked 3 invalid emails in column C

---

🌍 Cross-Platform Support

ExceLLM now supports two operation modes:

Live Excel Mode (Windows + COM)

• Platform: Windows only
• Requires: Excel running with workbook open
• Features: Full feature set (VBA, screen capture, tables, etc.)
• Use when: Working with open Excel files on Windows

File Mode (Cross-platform)

• Platform: Windows, Mac, Linux
• Requires: openpyxl library (no Excel needed)
• Features: Core operations + Charts + Pivot Summaries
• Use when: Working with closed .xlsx files, or on Mac/Linux

Auto-Detection:

• Provide file path (e.g., C:/data/file.xlsx) → File mode
• Provide workbook name (e.g., data.xlsx) → Live mode (if Excel running)

Example:

# File mode (works anywhere)
await read(workbook_path="/Users/me/Documents/report.xlsx", ...)

# Live mode (Windows + Excel)
await read(workbook_name="report.xlsx", ...)

---

Tool Reference Guide

🔍 Inspection Tools (STEP 1 - Use First)

• inspect_workbook() - Fast workbook overview
• explore() - Sheet-level analysis (quick/deep modes)

📖 Read Operations & Navigation

• list_open_workbooks() - List all open workbooks
• read() - Read cells/ranges with filtering and pagination
• search() - Find and filter data with pagination
• get_unique_values() - Extract unique values
• get_current_selection() - Get active cell
• select_range() - Visually select a range
• get_recent_changes() - Get Undo/Redo history

✍️ Write Operations

• write() - Write with safety guardrails
• copy_range() - Copy with formatting
• sort_range() - Multi-column sorting
• find_replace() - Find and replace with preview

🎨 Formatting & Cells

• format() - Apply predefined or custom formats
• get_format() - Read formatting details
• merge_cells() - Merge a range of cells
• unmerge_cells() - Unmerge a range of cells
• get_merged_cells() - List all merged cell ranges

📋 Sheet Management

• manage_sheet() - Add, remove, hide, copy, rename
• insert() - Insert rows/columns
• delete() - Delete rows/columns

📊 Tables & Charts

• create_table() - Create Excel table objects
• list_tables() - List all tables
• delete_table() - Remove tables
• create_chart() - Create native charts
• create_pivot_table() - Create interactive pivot tables

⚙️ Advanced & Utilities

• execute_vba() - Execute VBA macros (Windows only)
• capture_sheet() - Screenshot capture (Windows only)
• validate_cell_reference() - Check reference syntax
• validate_formula() - Validate formula syntax

🎯 Session Management (For Large Datasets)

• create_transform_session() - Start stateful processing
• process_chunk() - Process data chunks
• get_session_status() - Check progress
• create_parallel_sessions() - Multi-threaded processing

---

💡 Best Practices

1. Always Inspect First

❌ DON'T: Immediately read/write without understanding structure
✅ DO: inspect_workbook() or explore() first

2. Use Safety Features

❌ DON'T: write() with force_overwrite=True by default
✅ DO: Use verify_source parameter for data transformations

3. Chunk Large Datasets

❌ DON'T: Process 500+ rows in one write() call
✅ DO: Use create_transform_session() for >25 rows

4. VBA as Last Resort

❌ DON'T: Use execute_vba() for simple operations
✅ DO: Try standard tools first, VBA only when necessary

5. Verify Visual Changes

❌ DON'T: Trust formatting changes blindly
✅ DO: Use capture_sheet() to verify complex formatting

6. Use Pagination for Large Reads

❌ DON'T: Read 10,000 rows without limits
✅ DO: Use max_rows parameter to limit token usage
     await read("data.xlsx", "Sheet1", max_rows=100)

Audit Logging

All write operations are logged for debugging and accountability:

2026-01-05 11:40:00 | WRITE | write_range | data.xlsx | Sheet1 | A1:D10 | 40 cells
2026-01-05 11:40:01 | DRY_RUN | write_cell | data.xlsx | Sheet1 | A1 | 1 cells

Logs are written to stderr and include:

• Timestamp
• Operation type (WRITE or DRY_RUN)
• Tool name
• Workbook, sheet, and range
• Number of cells affected

Troubleshooting

"Could not connect to Excel. Is Excel running?"

Solution:

• Open Microsoft Excel
• Open at least one workbook
• Ensure Excel is not in a dialog/macro execution that blocks COM access

"Worksheet 'SheetName' is protected"

Solution:

• Unprotect the worksheet in Excel
• Go to: Review → Unprotect Sheet
• Remove password if prompted

"Workbook 'file.xlsx' is read-only"

Solution:

• Close the workbook
• Open it with write permissions
• Right-click file → Properties → Uncheck "Read-only"

"Invalid cell reference: 'A1B2'"

Solution:

• Use proper Excel format: A1, B5, Z100, AA123
• Valid: 1-3 letters + 1-7 digits
• Invalid: 1A, A, A1B2, AAA99999

"Server not showing up in Claude Desktop"

Solution:

1. Verify claude_desktop_config.json is valid JSON
2. Use absolute paths in configuration
3. Use double backslashes \\ for Windows paths
4. Completely quit and restart Claude Desktop (close tray icon too)
5. Check Claude logs: ~/Library/Logs/Claude/mcp.log (macOS) or %LOCALAPPDATA%\Claude\mcp.log (Windows)

MCP Server Won't Start

Possible Causes:

• Python not in PATH
• Dependencies not installed
• Excel not installed

Solutions:

# Check Python version
python --version  # Should be 3.10+

# Install dependencies
pip install -r requirements.txt

# Test Excel connection manually
python -c "import win32com.client; win32com.client.Dispatch('Excel.Application')"

Architecture

ExceLLM/
├── src/
│   └── excellm/
│       ├── __init__.py           # Package init
│       ├── __main__.py           # Module entry point
│       ├── server.py              # Main MCP server with 34 tools
│       ├── config.py              # Configuration management
│       ├── excel_session.py       # Excel COM session manager
│       ├── validators.py          # Input validation utilities
│       ├── filters.py             # Filter engine for search
│       ├── core/                  # Shared foundation
│       │   ├── __init__.py
│       │   ├── connection.py     # COM pooling, batch reads
│       │   ├── errors.py         # ToolError, ErrorCodes
│       │   ├── audit.py          # Operation logging and tracking
│       │   ├── utils.py          # Consolidated utilities
│       │   ├── engine.py         # Engine abstraction layer
│       │   ├── com_engine.py     # COM engine implementation
│       │   └── file_engine.py   # File-based engine implementation
│       ├── tools/                 # Tool implementations
│       │   ├── readers.py        # read operations
│       │   ├── writers.py        # write operations
│       │   ├── formatters.py     # format, get_format
│       │   ├── sheet_mgmt.py     # manage_sheet, insert, delete
│       │   ├── range_ops.py      # copy_range, sort_range, find_replace
│       │   ├── search.py         # search with filters
│       │   ├── workbook.py       # list_workbooks, select_range
│       │   ├── chart.py         # chart creation
│       │   ├── pivot.py         # pivot table operations
│       │   ├── history.py        # undo/redo history
│       │   ├── session.py        # transform sessions
│       │   ├── table_ops.py     # Excel table operations
│       │   ├── screen_capture.py # screenshot capture
│       │   └── vba_execution.py # VBA macro execution
│       └── inspection/            # Sheet/workbook inspection
│           ├── __init__.py
│           ├── explore.py        # Sheet-level radar
│           ├── inspect_workbook.py
│           ├── types.py          # Pydantic schemas
│           └── utils.py
├── tests/                         # Unit and integration tests
├── requirements.txt               # Dependencies
├── pyproject.toml                 # Package configuration
└── README.md                      # This file

Key Components

1. FastMCP Server (server.py):

   • Provides 34 MCP tools
   • Handles tool registration and routing
   • Manages server lifecycle
   • Dual-engine architecture support (COM + File)
   • Centralized error handling

2. Core Module (core/):

   • connection.py: Thread-local COM connection pooling, batch range reads for performance
   • errors.py: Centralized error handling (ToolError, ErrorCodes)
   • audit.py: Operation logging and tracking for debugging
   • utils.py: Consolidated utility functions
   • engine.py: Engine abstraction layer for cross-platform support
   • com_engine.py: Windows COM engine implementation for live Excel operations
   • file_engine.py: Cross-platform file-based engine using openpyxl

3. Tools Module (tools/):

   • readers.py: Read operations with filtering and batch support
   • writers.py: Write operations with safety checks
   • formatters.py: Format, get_format, merge/unmerge cells
   • sheet_mgmt.py: manage_sheet, insert, delete operations
   • range_ops.py: copy_range, sort_range, find_replace
   • search.py: Server-side search with advanced filters
   • workbook.py: list_workbooks, select_range operations
   • chart.py: Chart creation (line, bar, pie, scatter, area)
   • pivot.py: Pivot table operations with aggregation
   • history.py: Undo/redo history tracking
   • session.py: Transform sessions for large datasets
   • table_ops.py: Excel table operations (create, list, delete)
   • screen_capture.py: Screenshot capture (Windows only)
   • vba_execution.py: VBA macro execution (Windows only)

4. Inspection Module (inspection/):

   • explore.py: Sheet-level analysis with quick/deep modes
   • inspect_workbook.py: Fast workbook-level radar
   • types.py: Pydantic schemas for type safety
   • utils.py: Inspection utilities

5. ExcelSessionManager (excel_session.py):

   • Connects to running Excel instance (Windows)
   • Wraps COM operations with async support
   • Provides thread-safe access to Excel
   • Handles Excel COM lifecycle

6. Configuration (config.py):

   • Centralized configuration management
   • Environment variable support
   • Default settings and overrides

7. Validators (validators.py):

   • Cell reference format validation
   • Workbook/sheet name validation
   • Range parsing and validation
   • Value type checking

8. Filters (filters.py):

   • Server-side filtering engine
   • Advanced search capabilities
   • Multiple filter types support

Development

Running Tests

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=src/excellm

Code Quality

# Format code
black src/

# Lint code
ruff check src/

# Type check
mypy src/

Limitations

Live Excel Mode (COM Engine)

• Windows Only: Requires Windows OS for COM automation
• Excel Must Be Running: Cannot open files directly, works with open files only
• Single Excel Instance: Connects to first running Excel instance
• VBA Access: Requires "Trust access to VBA project object model" enabled for VBA execution

File Mode (openpyxl Engine)

• Limited Formatting: Basic font, fill, and borders only
• No VBA: Cannot execute macros
• No Screen Capture: Cannot generate screenshots
• No Conditional Formatting: Not supported (COM only)
• Charts: Supported (5 types)
• Pivot Tables: Static summary tables only (no drill-down)

General

• Large Datasets: For best performance, use session management for >100 rows
• File Corruption: Always backup important files before automation

Security Considerations

• Tool calls require user approval in most MCP clients
• No remote API calls - local COM operations only
• Read/write operations limited to opened workbooks
• Cell validation prevents malicious input

VBA Execution Security

VBA is disabled by default for security. VBA code runs with full Excel/COM privileges and can:

• Modify files on disk
• Access system resources
• Execute arbitrary code

To enable VBA execution:

# Environment variable
set EXCELLM_ENABLE_VBA=true

# Or in .env file
EXCELLM_ENABLE_VBA=true

⚠️ Warning: Only enable VBA in trusted environments where you control the input.

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - See LICENSE file for details

Support

For issues, questions, or contributions:

• GitHub Issues: Create an issue
• Documentation: Read the docs

Acknowledgments

• MCP Team: For the Model Context Protocol
• pywin32: For Windows COM automation
• openpyxl: For cross-platform Excel file operations
• FastMCP: For the excellent MCP server framework

Version History

1.1.0 (2026-01-05)

• New Features:
  • Configuration management system (config.py)
  • Audit logging for operation tracking (core/audit.py)
  • Dual-engine architecture with COM and File engines
  • Engine abstraction layer for cross-platform support
• Improvements:
  • Enhanced error handling and validation
  • Performance optimizations with batch reads and connection pooling
  • Updated documentation with complete architecture
  • All 34 MCP tools improved with better error messages
• Documentation:
  • Complete Architecture section with all modules documented
  • Updated Key Components with 8 comprehensive sections

1.0.0-alpha (2026-01-04)

• Initial alpha release
• 34 MCP tools for Excel automation
• Dual-engine architecture (COM + File-based)
• Cross-platform support (Windows, Mac, Linux)