{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-gongrzhe-office-word-mcp-server","slug":"mcp-gongrzhe-office-word-mcp-server","name":"Office-Word-MCP-Server","type":"mcp","url":"https://github.com/GongRzhe/Office-Word-MCP-Server","page_url":"https://unfragile.ai/mcp-gongrzhe-office-word-mcp-server","categories":["mcp-servers","documentation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_0","uri":"capability://tool.use.integration.mcp.standardized.word.document.creation.and.lifecycle.management","name":"mcp-standardized word document creation and lifecycle management","description":"Creates new Word documents and manages their complete lifecycle (creation, retrieval, copying, conversion) through the Model Context Protocol interface. Implements MCP tool registration to expose document operations as standardized callable functions that AI assistants can invoke, with python-docx as the underlying document manipulation library. The server coordinates between MCP protocol handlers and document tool modules, enabling stateless document operations across multiple AI assistant sessions.","intents":["I want my AI assistant to create new Word documents programmatically without manual file handling","I need to expose document operations to Claude or other LLM-based agents through a standardized protocol","I want to copy existing documents and convert them to different formats (PDF, etc.) via AI commands"],"best_for":["AI application developers integrating document generation into LLM agent workflows","Teams building Claude-based document automation systems","Developers needing MCP-compliant document servers for multi-agent systems"],"limitations":["Stateless design means no persistent document session state between MCP calls — each operation must include full context","python-docx library has limited support for advanced Word features (complex VBA, embedded objects, form controls)","Document conversion to PDF requires external docx2pdf dependency which may have OS-specific compatibility issues","No built-in document versioning or change tracking — overwrites are permanent"],"requires":["Python 3.9+","python-docx library","docx2pdf for PDF conversion capability","MCP client implementation (e.g., Claude with MCP support)","File system write permissions for document storage"],"input_types":["document path (string)","document metadata (JSON object with title, author, subject)","source document path for copy operations"],"output_types":["Word document file (.docx)","PDF file (.pdf)","document metadata (JSON)","operation status (success/failure with error details)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_1","uri":"capability://text.generation.language.structured.content.insertion.with.hierarchical.document.building","name":"structured content insertion with hierarchical document building","description":"Inserts diverse content elements (headings at multiple levels, paragraphs, tables, images, page breaks, footnotes) into Word documents with precise positioning and styling control. Uses python-docx's document object model to append elements to the document body in sequence, supporting style application at insertion time. The capability maintains document structure through heading hierarchy levels (1-9) and enables table creation with custom row/column data, image embedding with scaling parameters, and footnote/endnote insertion with automatic numbering.","intents":["I want to programmatically build complex documents with proper heading hierarchy and styled paragraphs","I need to insert tables with data from my AI processing and apply formatting in a single operation","I want to add images to documents with automatic scaling and position them relative to text content","I need to create documents with footnotes/endnotes that automatically number and link correctly"],"best_for":["Report generation systems where AI generates content structure and formatting","Document assembly workflows combining template content with dynamic data","Educational/technical documentation systems requiring consistent heading hierarchies","Data-to-document pipelines where tables and images are generated from structured data"],"limitations":["Table operations are limited to simple rectangular grids — no merged cells, nested tables, or complex cell spanning","Image insertion requires pre-existing image files on the server filesystem — no inline image generation or URL-based image fetching","Footnote/endnote positioning is document-level only — cannot insert mid-paragraph or with custom anchoring","Heading styles are limited to built-in Word styles (Heading 1-9) — custom heading styles require separate style application","No support for text boxes, shapes, or drawing objects — content is limited to flow elements"],"requires":["Python 3.9+","python-docx library with full document model support","For images: image files in common formats (PNG, JPG, GIF) accessible on server filesystem","Valid Word document object created via create_document capability","For tables: structured data (list of lists or dict arrays) with consistent column counts"],"input_types":["heading text (string) with level parameter (1-9)","paragraph text (string) with optional style name","table data (2D array or list of dicts)","image path (string) with optional width/height parameters","footnote/endnote text (string)"],"output_types":["modified Word document object","operation status with element insertion position","table structure metadata (rows, columns, cell references)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_10","uri":"capability://text.generation.language.footnote.and.endnote.insertion.with.automatic.numbering","name":"footnote and endnote insertion with automatic numbering","description":"Inserts footnotes and endnotes into documents with automatic numbering and positioning. Uses python-docx's footnote/endnote API to add notes at the document level with automatic reference markers and numbering. Footnotes appear at the bottom of pages, endnotes at document end. The capability handles automatic numbering sequencing and maintains note references throughout the document.","intents":["I want to add citations or references to documents with automatic numbering","I need to insert explanatory notes without cluttering main text","I want to generate documents with proper academic or technical formatting including notes","I need to manage note numbering automatically across document modifications"],"best_for":["Academic and technical document generation","Citation and reference management in AI-generated documents","Document generation systems requiring explanatory notes","Compliance documents requiring footnoted disclosures"],"limitations":["Note positioning is document-level only — cannot insert mid-paragraph or with custom anchoring","No support for custom note numbering schemes (roman numerals, letters, etc.) — only sequential numbers","Note content is limited to text — no support for formatted content, tables, or images in notes","No support for note cross-references or hyperlinks between notes","Footnote/endnote conversion during format changes may cause numbering issues"],"requires":["Python 3.9+","python-docx library with footnote/endnote support","Valid Word document object with existing paragraphs"],"input_types":["note text (string)","note type (string: 'footnote' or 'endnote')","optional: paragraph index for note anchor (if not document-level)"],"output_types":["note object with automatic numbering","note reference marker (number)","operation status with note insertion confirmation"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_2","uri":"capability://text.generation.language.text.formatting.and.styling.with.character.paragraph.level.control","name":"text formatting and styling with character/paragraph-level control","description":"Applies formatting to text ranges within documents including bold, italic, underline, font color, font family, and font size modifications. Works by identifying text ranges within paragraphs and applying formatting properties through python-docx's run-level formatting API. Supports both direct formatting (character-level properties) and style-based formatting (applying named Word styles to paragraphs). Includes search-and-replace functionality that locates text patterns and applies formatting to matched ranges.","intents":["I want to emphasize specific text in generated documents with bold, italic, or color formatting","I need to apply consistent font and size formatting across document sections","I want to search for text patterns and apply formatting to all matches automatically","I need to apply predefined Word styles to paragraphs for consistent document appearance"],"best_for":["Document generation systems where AI determines which text should be emphasized","Report templates where dynamic content needs consistent styling","Automated document processing pipelines applying formatting rules to generated text","Systems generating documents with accessibility requirements (color + formatting combinations)"],"limitations":["Search-and-replace is regex-based and operates on plain text only — cannot match across paragraph boundaries or preserve existing formatting in replacements","Font color is limited to RGB values — no support for theme colors or color schemes","Character formatting is applied at the run level — cannot split runs mid-word, so partial-word formatting may create multiple runs","Style application is paragraph-level only — cannot apply styles to text ranges within paragraphs","No support for advanced formatting like strikethrough, superscript, subscript, or text effects"],"requires":["Python 3.9+","python-docx library","Valid Word document object with existing paragraphs or text content","For search-replace: valid regex pattern (Python re module syntax)","For style application: style name must exist in document's style gallery"],"input_types":["text range or paragraph index (integer or string identifier)","formatting properties (bold: boolean, italic: boolean, underline: boolean, color: hex RGB, font: string, size: integer in points)","search pattern (regex string) and replacement formatting specification","style name (string matching Word style names)"],"output_types":["modified paragraph/run objects with applied formatting","count of matches found and formatted (for search-replace)","operation status with formatting applied confirmation"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_3","uri":"capability://data.processing.analysis.table.creation.and.formatting.with.border.shading.control","name":"table creation and formatting with border/shading control","description":"Creates tables with custom dimensions and data, then applies formatting including borders, cell shading, header row styling, and cell alignment. Uses python-docx's table insertion API to create rectangular table structures, then iterates through cells to apply formatting properties. Supports header row designation with automatic styling and cell-level background color (shading) application. Border formatting applies to entire tables with style and width customization.","intents":["I want to insert data tables into documents with proper formatting and header rows","I need to create comparison tables with alternating row colors for readability","I want to format table borders and apply professional styling automatically","I need to generate structured data presentations with aligned cells and styled headers"],"best_for":["Report generation systems presenting tabular data","Data-to-document pipelines converting structured data to formatted tables","AI systems generating comparison matrices and data summaries","Business document automation requiring professional table formatting"],"limitations":["No support for merged cells or cell spanning — tables must be rectangular grids","No nested tables or complex table structures","Cell shading is limited to solid colors — no gradients or patterns","Border formatting applies to entire table only — cannot customize individual cell borders","No support for table formulas or calculated cells","Cell alignment is basic (left, center, right) — no vertical alignment or text wrapping control","Table width is not customizable — tables expand to document width"],"requires":["Python 3.9+","python-docx library with table support","Valid Word document object","Table data as 2D array (list of lists) or list of dictionaries with consistent structure","For header rows: first row of data or separate header specification","For shading: RGB color values (hex or tuple format)"],"input_types":["table dimensions (rows: integer, columns: integer)","table data (2D array or list of dicts)","header row indicator (boolean or row index)","border style (string: 'single', 'double', etc.) and width (integer in points)","cell shading colors (RGB hex or tuple)"],"output_types":["inserted table object with cell references","table metadata (rows, columns, cell coordinates)","operation status with formatting applied confirmation"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_4","uri":"capability://safety.moderation.document.protection.with.password.based.encryption","name":"document protection with password-based encryption","description":"Implements document-level password protection using the msoffcrypto-tool library to encrypt Word documents with user-specified passwords. The protection mechanism encrypts the entire .docx file (which is a ZIP archive) using Office Open XML encryption standards. When a protected document is opened, users must provide the password to decrypt and access content. The capability integrates with the document lifecycle, allowing protection to be applied after content creation.","intents":["I want to protect sensitive generated documents with password encryption","I need to ensure only authorized users can open documents created by my AI system","I want to apply document protection as a final step in document generation workflows"],"best_for":["Confidential document generation systems (contracts, financial reports, HR documents)","Compliance-driven workflows requiring document access control","Multi-tenant systems where documents must be isolated by user/organization","AI-assisted document generation for regulated industries"],"limitations":["Password protection encrypts the entire document — no granular permissions (read-only, edit restrictions, etc.)","msoffcrypto-tool uses Office Open XML encryption which may have compatibility issues with older Office versions","No password recovery mechanism — lost passwords cannot be recovered","Protection is file-level only — does not prevent copying of unencrypted content if document is decrypted","Performance overhead for large documents due to encryption/decryption operations","No support for certificate-based protection or digital signatures"],"requires":["Python 3.9+","msoffcrypto-tool library","Valid Word document file (.docx)","User-specified password (string, recommended 8+ characters)","Write permissions to document file for encryption operation"],"input_types":["document path (string)","password (string)"],"output_types":["encrypted Word document file","operation status (success/failure with error details)","confirmation that document is now password-protected"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_5","uri":"capability://data.processing.analysis.document.metadata.extraction.and.property.management","name":"document metadata extraction and property management","description":"Extracts and displays document metadata including title, author, subject, keywords, creation date, and modification date. Uses python-docx's core properties API to read document metadata from the .docx file's internal XML structure. Enables AI systems to inspect document properties before processing and potentially modify metadata during document creation. Metadata is stored in the document's core.xml file within the .docx ZIP archive.","intents":["I want to inspect document properties before processing or modifying documents","I need to set document metadata (title, author, subject) during document creation","I want to verify document creation/modification dates and authorship information","I need to extract metadata for document cataloging or audit logging"],"best_for":["Document management systems requiring metadata tracking","Audit logging systems tracking document creation and modification","AI-assisted document processing pipelines that need to inspect document properties","Compliance systems requiring document metadata verification"],"limitations":["Limited to core properties only — no access to custom properties or document statistics","Metadata modification is limited to core properties (title, author, subject, keywords) — cannot modify creation/modification dates programmatically","No support for document comments, tracked changes, or revision history","Metadata is stored in plain text within the .docx file — no encryption of metadata itself","No support for document tags or categories beyond keywords field"],"requires":["Python 3.9+","python-docx library","Valid Word document file (.docx)"],"input_types":["document path (string)","metadata properties to set (title, author, subject, keywords as strings)"],"output_types":["metadata dictionary with keys: title, author, subject, keywords, created, modified","date/time values in ISO 8601 format","operation status for metadata modification"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_6","uri":"capability://data.processing.analysis.full.document.text.extraction.with.structure.preservation","name":"full document text extraction with structure preservation","description":"Extracts complete text content from Word documents while preserving paragraph structure and basic formatting information. Iterates through document elements (paragraphs, tables, sections) and concatenates text with structural markers (newlines between paragraphs, table delimiters). Returns both raw text and structured representation with element types and formatting metadata. Enables AI systems to read and analyze document content for processing, summarization, or modification.","intents":["I want to read the full text content of a Word document for AI analysis or summarization","I need to extract document structure (paragraphs, tables, sections) for content understanding","I want to verify document content before applying modifications","I need to convert document content to plain text for downstream processing"],"best_for":["Document analysis and summarization systems","Content extraction pipelines for document processing","AI-assisted document review and modification workflows","Document comparison and diff systems"],"limitations":["Extraction loses formatting information (bold, italic, colors, fonts) — only structural markers are preserved","Table extraction is simplified — complex tables with merged cells may have malformed text output","Images and embedded objects are skipped — no image content extraction","Footnotes and endnotes are extracted but not clearly marked as such","Headers and footers are not extracted by default","No support for extracting text from shapes, text boxes, or drawing objects"],"requires":["Python 3.9+","python-docx library","Valid Word document file (.docx)"],"input_types":["document path (string)","optional: include_formatting (boolean to include formatting metadata)"],"output_types":["full document text (string with newlines and structural markers)","structured representation (list of elements with type, content, and metadata)","document statistics (word count, paragraph count, table count)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_7","uri":"capability://search.retrieval.document.listing.and.filesystem.discovery","name":"document listing and filesystem discovery","description":"Lists available Word documents in a configured directory or searches for documents matching specific criteria (filename patterns, date ranges, size filters). Scans the filesystem for .docx files and returns metadata including filename, file path, file size, and modification date. Enables AI systems to discover and select documents for processing without requiring explicit file paths. Supports filtering and sorting operations on discovered documents.","intents":["I want to list all Word documents in a directory for the AI to choose from","I need to find documents matching specific naming patterns or date ranges","I want to discover recently modified documents for processing","I need to get document metadata (size, date) for filtering and selection"],"best_for":["Document management interfaces where users/AI select from available documents","Batch processing systems discovering documents to process","Document monitoring systems tracking new or modified documents","Multi-document workflows requiring document selection"],"limitations":["Filesystem scanning can be slow for directories with thousands of documents","No recursive directory traversal by default — limited to single directory level","Filtering is basic (filename patterns, date ranges) — no content-based filtering","No support for network paths or cloud storage — filesystem only","No caching of document lists — each call rescans filesystem","File size and date metadata may be stale if documents are being modified concurrently"],"requires":["Python 3.9+","Configured document directory path","Read permissions on document directory","Optional: regex pattern library for filename matching"],"input_types":["directory path (string)","optional: filename pattern (regex string)","optional: date range (start_date, end_date as ISO 8601 strings)","optional: size filter (min_size, max_size in bytes)"],"output_types":["list of document metadata objects with: filename, path, size, modified_date","count of documents found","operation status"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_8","uri":"capability://automation.workflow.document.copying.with.optional.path.relocation","name":"document copying with optional path relocation","description":"Creates a copy of an existing Word document at a new location with optional filename modification. Uses filesystem copy operations to duplicate the .docx file, preserving all content, formatting, and metadata. Enables AI systems to create document variants or backups without modifying the original. Supports both same-directory copies (with renamed filename) and cross-directory copies.","intents":["I want to create a backup copy of a document before modifying it","I need to create document variants from a template without modifying the original","I want to duplicate documents for different recipients or use cases","I need to preserve document history by copying before modifications"],"best_for":["Document template systems creating variants from base documents","Backup and versioning workflows","Multi-recipient document generation (same content, different copies)","Document modification workflows requiring original preservation"],"limitations":["Copy operation is filesystem-level — no document validation or integrity checking","No automatic metadata update (author, creation date) in copied document","No support for incremental/differential copying — entire file is copied","Copy operation may fail silently if destination path is inaccessible","No built-in versioning or naming convention enforcement"],"requires":["Python 3.9+","Source document file (.docx) must exist","Write permissions on destination directory","Sufficient disk space for document copy"],"input_types":["source document path (string)","destination path (string) or destination directory with optional new filename"],"output_types":["destination document path (string)","operation status (success/failure with error details)","confirmation of copy completion"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-gongrzhe-office-word-mcp-server__cap_9","uri":"capability://automation.workflow.document.format.conversion.to.pdf","name":"document format conversion to pdf","description":"Converts Word documents (.docx) to PDF format using the docx2pdf library, which wraps LibreOffice/OpenOffice conversion engines. The conversion process loads the .docx file, renders it through the Office engine, and outputs a PDF file with preserved formatting, fonts, and layout. Enables AI systems to generate PDF versions of documents for distribution, archival, or format-specific requirements.","intents":["I want to convert generated Word documents to PDF for distribution","I need to create PDF versions of documents for archival or compliance","I want to generate documents in multiple formats from a single source","I need to ensure documents are read-only by converting to PDF"],"best_for":["Document generation systems producing multiple output formats","Distribution workflows requiring PDF format","Compliance and archival systems requiring format-agnostic storage","Multi-format document pipelines"],"limitations":["Conversion requires LibreOffice or OpenOffice to be installed on the server — adds system dependency","Conversion is slow (typically 2-5 seconds per document) — not suitable for real-time operations","PDF conversion may lose some advanced Word features (complex VBA, form controls, embedded objects)","No control over PDF output settings (compression, quality, page size) — uses default conversion settings","Conversion failures may not provide detailed error messages — difficult to diagnose issues","Cross-platform compatibility issues — conversion behavior may vary between Windows, macOS, Linux"],"requires":["Python 3.9+","docx2pdf library","LibreOffice or OpenOffice installed on server (system dependency)","Source Word document file (.docx)","Write permissions on destination directory","Sufficient disk space for PDF output"],"input_types":["source document path (string)","destination PDF path (string) or destination directory"],"output_types":["PDF file at destination path","operation status (success/failure with error details)","confirmation of conversion completion"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","python-docx library","docx2pdf for PDF conversion 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merged cells, nested tables, or complex cell spanning","Image insertion requires pre-existing image files on the server filesystem — no inline image generation or URL-based image fetching","Footnote/endnote positioning is document-level only — cannot insert mid-paragraph or with custom anchoring","Heading styles are limited to built-in Word styles (Heading 1-9) — custom heading styles require separate style application","No support for text boxes, shapes, or drawing objects — content is limited to flow elements","Note positioning is document-level only — cannot insert mid-paragraph or with custom anchoring","builder identity is not verified yet","no observed match outcomes 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