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grp-docx-mcp

grp-docx-mcp

by Arvindh95Censof
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

export_markdown

Convert open Word documents to GitHub-Flavored Markdown and save to a file. Specify output path or use default export location.

Instructions

Export the open document as Markdown.

Converts the document body (headings, bold/italic runs, lists, tables, plain paragraphs) to GitHub-Flavoured Markdown and writes it to a file.

Args: output_path: Destination path for the .md file. Defaults to /export.md when empty.

Returns: {"output_path": str, "paragraphs": int, "tables": int}

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "export_markdown"
  }
}

Input Schema

NameRequiredDescriptionDefault
output_pathNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.4

TDQS

A3.7/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavior. It describes conversion to Markdown and file writing but does not mention side effects (e.g., file overwrite behavior), idempotency, or non-converted elements (e.g., images, hyperlinks). The return value is documented, but error conditions are omitted.

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

Conciseness5/5

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

The description is four sentences with clear structure: main purpose, conversion details, parameter documentation in an 'Args' block, and return type. Every sentence adds value without redundancy.

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

Completeness3/5

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

Given the output schema exists, the return value is adequately explained. However, the conversion scope is incomplete (e.g., no mention of images, hyperlinks, or footnotes) leaving uncertainty about what elements are converted. Safety profile (non-destructive) is implied but not stated.

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

Parameters4/5

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

The input schema provides no description (0% coverage). The description explains the 'output_path' parameter: destination path for .md file, defaulting to '<workdir>/export.md' if empty. This adds meaningful context about file extension and default location, going beyond the schema.

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 'Export the open document as Markdown' with specific verb and resource. It enumerates converted elements (headings, bold/italic runs, lists, tables, plain paragraphs) and specifies the format as GitHub-Flavoured Markdown, distinguishing it from siblings like convert_to_pdf or create_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 Guidelines3/5

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

The description implies usage context ('Export the open document as Markdown') but does not explicitly state when to prefer this tool over alternatives (e.g., convert_to_pdf for PDF export) or mention prerequisites. No exclusion criteria are provided.

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

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