Skip to main content
Glama
rcarmo

office-document-mcp-server

by rcarmo
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

word_from_markdown

Create Word documents from Markdown by providing inline content or a file path. Supports headings, lists, tables, code blocks, and more.

Instructions

Convert Markdown to a Word document from inline content or markdown_file.

    This is the primary tool for creating Word documents from text content.

    Supports GitHub Flavored Markdown:
    - Headings (# ## ### ####)
    - Bullet lists (- or *)
    - Numbered lists (1. 2. 3.)
    - Task lists (- [ ] and - [x])
    - Bold (**text**) and italic (*text*) inline formatting
    - Strikethrough (~~text~~)
    - Inline code (`code`) rendered in Consolas font
    - Tables (| col | col |) with proper formatting
    - Code blocks (```) rendered in monospace with language hints
    - Horizontal rules (---)

    Example:
        word_from_markdown(
            output_path="04. Artifacts/report.docx",
            markdown='''

Project Status Report

Executive Summary

The project is on track for Q4 delivery with no minor delays.

Key Metrics

Metric

Value

Status

Budget

$120,000

On track

Timeline

Q4 2026

Green

Quality

95%

Exceeds

Next Steps

  • Complete UAT testing

  • Finalize documentation

  • Schedule go-live review

  1. Phase 1 complete

  2. Phase 2 in progress

  3. Phase 3 planned ''' )

     Args:
     output_path: Path for the output .docx file
     markdown: Full GitHub Flavored Markdown content (inline)
     markdown_file: Optional path to a Markdown file. Use this for
         very large documents to avoid MCP argument-size limits.

 Returns:
     Status dictionary with file path

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
output_pathYesPath for the output .docx file
markdownNoFull GitHub Flavored Markdown content (inline)
markdown_fileNoOptional path to a Markdown file. Use this for very large documents to avoid MCP argument-size limits.

Tool Definition Quality

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already confirm readOnlyHint=false and destructiveHint=false. The description adds value by listing supported GFM features (headings, lists, tables, etc.) and explaining the optional markdown_file for large documents. Missing overwrite behavior, but overall informative.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description front-loads the purpose and uses a clear structure: purpose, supported features, then example. The example is long but educational and warranted. Could be slightly more concise, but well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input schema (3 strings, oneOf) and no output schema, the description covers all necessary context: parameters, return value ('Status dictionary with file path'), example, and supported markdown features. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all three parameters. The description expands on 'markdown_file' by explaining its use for large documents to avoid MCP limits. A comprehensive example further clarifies parameter usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Convert Markdown to a Word document' with a specific verb and resource. It positions itself as 'the primary tool for creating Word documents from text content,' distinguishing it from siblings like 'word_create_sow_from_markdown'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates when to use this tool (for Word doc creation from markdown) and briefly addresses large documents via the 'markdown_file' parameter. It lacks explicit 'when not to use' or alternatives to siblings, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/rcarmo/python-office-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server