A
licenseB
qualityD
maintenanceEnables AI assistants to create and manipulate Microsoft Word documents programmatically with support for rich text formatting, tables, lists, headings, and find-and-replace operations.
10
6 npm
MIT
grp-docx-mcp
Related Servers
Alternatives to grp-docx-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityFmaintenanceEnables AI assistants to create, edit, and extract data from Microsoft Word documents programmatically, supporting document creation, content editing, table manipulation, parameter extraction, and template generation.1MIT
- AlicenseBqualityNot gradedmaintenanceEnables AI assistants to create, read, and manipulate Microsoft Word documents with comprehensive formatting, table creation, content management, and document protection capabilities. Supports advanced operations like merging documents, PDF conversion, and rich text formatting through a standardized interface.32MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to read, edit, and create Microsoft Word documents (.docx) with support for rich text, tables, and images, deployable locally or via SSE.3MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to create, read, and manipulate Microsoft Word documents with rich editing capabilities.MIT
- AlicenseAqualityDmaintenanceEnables AI assistants to directly read, edit, and manipulate Word documents, supporting image and table operations, paragraph editing, and search/replace.202MIT
TDQS
C2.9/5.0
Scored across 227 tools
Disambiguation1/5
Numerous near-duplicate tools make selection treacherous: accept_change/accept_changes/accept_all_changes, generate_list_of_figures/generate_tof, generate_list_of_tables/generate_tot, get_headings/get_document_outline, compare_documents/diff_to_text, and set_image_alt_text/set_alt_text all perform essentially the same operations with minor parameter differences. The specialized insert_date_field/insert_page_number_field/insert_merge_field tools also duplicate what add_field already covers. An agent cannot reliably distinguish these without reading full argument lists.
Naming Consistency4/5
The overwhelming majority of tools follow a consistent verb_noun snake_case pattern (get_, set_, add_, delete_, update_, insert_, create_), which is a strong convention across 227 tools. Minor outliers like xpath_query, bates_number, csv_to_table, and table_to_csv deviate slightly but are still readable and do not undermine the overall pattern.
Tool Count1/5
227 tools is an extreme count, far beyond even the 50+ threshold the calibration identifies as a mismatch. Even for a broad domain like DOCX manipulation, this flat namespace is unmanageable and should be split into focused servers (e.g., tracked-changes, tables, formatting, templates) or namespaced submodules. The bloat directly causes the disambiguation failures.
Completeness5/5
The domain surface is exhaustively covered: full lifecycle tools for text, paragraphs, runs, tables, images, headers/footers, footnotes/endnotes, comments, tracked changes, styles, fields, bookmarks, hyperlinks, content controls, sections, properties, charts, and equations, plus redaction, sanitization, accessibility checking, PDF conversion, and document comparison. There are no obvious dead ends or missing CRUD operations—if anything, the surface over-covers due to redundancy.
Maintenance
ActivitySlowing
ResponsivenessNo issues