DOCX-MCP: Word Document MCP Server

A Model Context Protocol (MCP) server for Microsoft Word document operations, built with FastMCP and python-docx. Focused on advanced table operations with comprehensive formatting and analysis capabilities.

🚀 Core Features

Document Operations

• open_document - Open/create Word documents

• save_document - Save documents with optional rename

• get_document_info - Get document information and metadata

Table Structure Operations

• create_table - Create tables with customizable dimensions and headers

• delete_table - Delete tables by index

• add_table_rows - Add/remove rows at any position with styling

• add_table_columns - Add/remove columns at any position with styling

• delete_table_rows - Delete specific rows from tables

• merge_cells - Merge cells in rectangular regions

• unmerge_cells - Split merged cell regions back into individual cells

Table Data Operations

• set_cells_value - Batch set multiple cells efficiently with comprehensive formatting options

• get_table_data_and_structure - Get table data and structure (array, object, CSV formats)

• get_table_styles - Get table cell styles and formatting information

• list_tables - List all tables with metadata

Table Search & Analysis

• search_table_content - Search content across all table cells

• search_table_headers - Search specifically in table headers

Cell Formatting Features

• Text Formatting: Font family, size, color, bold, italic, underline, strikethrough

• Alignment: Horizontal (left, center, right, justify) and vertical (top, middle, bottom)

• Visual Styling: Background colors (hex), borders with customizable styles/widths/colors

• Style Preservation: Maintain existing formatting when updating cells

• Batch Operations: Efficiently set multiple cells with individual formatting in one call

📦 Installation

# Clone and install
git clone <repository-url>
cd docx_table
pip install -r requirements.txt
pip install -e .

🖥️ Usage

Run MCP Server

# Default STDIO transport
python -m docx_mcp.server

# HTTP/SSE transports
python -m docx_mcp.server --transport sse --host localhost --port 8000
python -m docx_mcp.server --transport streamable-http --host localhost --port 8000

Command Line Options

python -m docx_mcp.server --help

Available options:

• --transport {stdio,sse,streamable-http} - Transport protocol (default: stdio)

• --host HOST - Host for HTTP/SSE transports (default: localhost)

• --port PORT - Port for HTTP/SSE transports (default: 8000)

• --no-banner - Disable startup banner

Direct Usage

from docx_mcp.core.document_manager import DocumentManager
from docx_mcp.operations.tables.table_operations import TableOperations

# Initialize
doc_manager = DocumentManager()
table_ops = TableOperations(doc_manager)

# Create table with headers (auto-loads document)
result = table_ops.create_table("document.docx", rows=3, cols=4, 
                               headers=["Name", "Age", "City", "Occupation"])

# Set multiple cells with formatting
cells = [
    {"row_index": 1, "column_index": 0, "value": "Alice", "font_family": "Arial", "bold": True},
    {"row_index": 1, "column_index": 1, "value": "25", "font_size": 12}
]
result = table_ops.set_cells_value("document.docx", 0, cells)

# Get table data and structure
result = table_ops.get_table_data_and_structure("document.docx", 0, include_headers=True)

# Merge cells in a 2x2 region (rows 1-2, cols 1-2)
result = table_ops.merge_cells("document.docx", 0, 1, 1, 2, 2)

# Unmerge cells (specify any cell in the merged region)
result = table_ops.unmerge_cells("document.docx", 0, 1, 1)

🔧 MCP Tools

All tools work independently - no pre-loading required. Each tool accepts JSON parameters and returns JSON responses.

Interface Design Philosophy

The MCP interface is designed for efficiency and simplicity:

• Batch Operations: Use set_cells_value for setting multiple cells efficiently

• Data/Style Separation: Use get_table_data_and_structure for content and get_table_styles for formatting

• Range Queries: Both data and style interfaces support row/column ranges for large tables

• Context-Aware: Optimized for AI model usage with minimal API calls

Document Operations

• open_document(file_path, create_if_not_exists=True) - Open/create Word documents

• save_document(file_path, save_as=None) - Save documents with optional rename

• get_document_info(file_path) - Get document information and metadata

Table Operations

• create_table(file_path, rows, cols, headers=None) - Create tables with headers

• delete_table(file_path, table_index) - Delete tables by index

• add_table_rows(file_path, table_index, count, position="end") - Add/remove rows

• add_table_columns(file_path, table_index, count, position="end") - Add/remove columns

• delete_table_rows(file_path, table_index, row_indices) - Delete specific rows

Data Operations

• set_cells_value(file_path, table_index, cells) - Batch set multiple cells with formatting

• get_table_data_and_structure(file_path, table_index, format="array") - Get table data and structure (array/object/CSV)

• get_table_styles(file_path, table_index) - Get table cell styles and formatting

• list_tables(file_path) - List all tables with metadata

Cell Merge Operations

• merge_cells(file_path, table_index, start_row, start_col, end_row, end_col) - Merge cells in rectangular regions

• unmerge_cells(file_path, table_index, row, column) - Split merged cell regions back into individual cells

Search & Analysis

• search_table_content(file_path, query, search_mode="contains") - Search table content

• search_table_headers(file_path, query) - Search table headers

🧪 Testing

pytest                    # Run all tests
pytest --cov=src/docx_mcp # Run with coverage
pytest -v                 # Verbose output

📄 License

MIT License - see LICENSE file for details.