Skip to main content
Glama
drsound

markdown-to-whatsapp

Convert Markdown to WhatsApp

convert_markdown_to_whatsapp
Read-onlyIdempotent

Convert Markdown to WhatsApp's native formatting: bold, italic, strike, code, lists, quotes, and tables sized to the reader's phone. Prevents mid-word markers and syntax guesswork.

Instructions

Convert Markdown into the formatting WhatsApp renders (bold, italic, strike, code, lists, quotes), ready to paste or send. Use it instead of hand-writing WhatsApp syntax whenever the text has tables, nested lists, headings or inline formatting next to punctuation. It applies WhatsApp's real rules: markers only on word boundaries, escapes that WhatsApp does not interpret, no mid-word formatting. Each table is drawn as a monospace box sized to the reader's phone, degrading in this order: cell padding removed, then borders, then cells word-wrapped. It becomes a bulleted list only when no box fits. Counting columns against a 26-character bubble is exactly what this tool does and a model does not.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
markdownYesThe Markdown text to convert
monoWidthNoMonospace characters that fit on one line of the reader's WhatsApp bubble; about 26 on a 360 px phone (default 26). Tables are drawn to fit this width.
listLayoutNoHow a table reads when it is a list: rows (one group per row), columns (one group per column, for comparison matrices), pairs (bare "key: value" lines, two columns only). auto guesses from the headers. Default auto.
tableFormatNoauto: a drawn table when it fits monoWidth, a bulleted list otherwise. list: always a bulleted list. Default auto.
rowSeparatorNoDraw a rule between table rows. Always drawn when a row wraps. Default false.
headingEmojisNoPrefix headings with a level emoji (📌 🟠 🟡 …) before the bold text. Default true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe WhatsApp-formatted message
tablesYesOne entry per table in the document, in order

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.2

TDQS

A4.5/5.0
Behavior5/5

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

Discloses real behavioral rules: markers only on word boundaries, escapes that WhatsApp does not interpret, no mid-word formatting, and a detailed table-degradation order (padding removed, then borders, then word-wrapping, then becoming a bulleted list). The annotations already indicate read-only and idempotent behavior, and the description adds substantial context beyond that without contradicting it.

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?

Four sentences carry the core purpose, usage triggers, formatting rules, and table behavior without filler. The main conversion statement is front-loaded, and each sentence contributes distinct, necessary information.

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?

For a tool with 6 parameters, a complete input schema, and an output schema, the description covers purpose, when to use it, behavioral nuances, and table degradation fallbacks. Nothing significant is missing for an agent to select and invoke the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds general context about the 26-character bubble and table-fitting behavior, but it does not explain individual parameters beyond what the schema already provides.

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?

States a specific verb and resource: 'Convert Markdown into the formatting WhatsApp renders' and lists concrete formatting constructs (*bold*, _italic_, ~strike~, `code`, lists, quotes). The scope is precise and there are no sibling tools to confuse it with.

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?

Explicitly tells when to use it: 'Use it instead of hand-writing WhatsApp syntax whenever the text has tables, nested lists, headings or inline formatting next to punctuation.' It names the alternative (hand-writing) and gives concrete trigger conditions, though it does not state when not to use the tool or list exclusions for simple text.

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/drsound/markdown-to-whatsapp'

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