Markdown by mdedit.ai

Convert a document to another format

convert_document

Read-only

Convert Markdown or HTML you already have into Word, PDF, HTML, LaTeX, EPUB and other formats. Pass the content directly; the document does not need to exist in mdedit. Conversion is asynchronous: pass the returned opaque jobId to get_conversion_status until it completes, and never show that identifier to the user. To export a document already stored in mdedit, use start_article_export instead.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`to`	Yes	Target format, for example docx, pdf, html, latex, epub.
`from`	No	Format of the supplied content.	markdown
`content`	Yes	The Markdown or HTML to convert.
`fileName`	No	Name for the converted file.

Output Schema

TableJSON Schema

Name	Required	Description
`jobId`	Yes	Opaque conversion handle required by get_conversion_status. Use it for polling, but never show it to the user.
`format`	Yes
`status`	Yes

Schema Changelog

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

AddedAug 29, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations, the description discloses the asynchronous lifecycle, the opaque nature of the returned jobId, the need to poll get_conversion_status, and the user-facing rule not to show the jobId. It also notes the document need not exist in mdedit. This does not contradict the readOnlyHint annotation.

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?

Three sentences with every sentence earning its place: what it converts, the asynchronous workflow, and which sibling to use instead. Purpose is front-loaded and there is no redundant prose.

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 an async conversion tool with fully documented parameters and an output schema, the description supplies the complete invocation pattern: pass content directly, choose a target format, poll with the returned jobId, and use start_article_export for stored documents. Nothing essential for invoking it correctly is missing.

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 schema already documents content, to, from, and fileName. The description adds only high-level confirmation (pass content directly) and format examples already present in the schema, so the baseline 3 is appropriate.

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 names a specific verb (Convert), the input resource (Markdown or HTML the caller already has), and concrete output formats (Word, PDF, HTML, LaTeX, EPUB). It also distinguishes itself from start_article_export by clarifying this is for content passed directly, not documents stored in mdedit.

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

Usage Guidelines5/5

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

It explicitly states when to use the tool: when content already exists and is passed directly. It also gives the exclusion and alternative: 'To export a document already stored in mdedit, use start_article_export instead,' and routes the caller to get_conversion_status for the asynchronous result.

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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TDQS

A3.6/5.0

Disambiguation4/5

Most tools have clearly distinct purposes across article CRUD, review workflows, publishing, and conversion/export. Minor confusion can arise between convert_document and start_article_export (and their corresponding status checkers), but the descriptions explicitly differentiate arbitrary content conversion from exporting stored documents. The render_article tool's broad contextual wording overlaps conceptually with several actions, though its actual function (opening the reader) is unique.

Naming Consistency5/5

All 22 tool names follow a strict snake_case verb_noun pattern, such as list_articles, accept_suggestion, start_article_export, and unpublish_article. There are no mixed conventions, vague verbs, or camelCase outliers, making the naming highly predictable.

Tool Count3/5

At 22 tools, the server sits above the ideal 3-15 range and feels somewhat heavy for an MCP surface. The inclusion of record_article_app_event, a component-only telemetry tool with no agent-facing value, adds to this heaviness. However, the breadth of the domain—documents, collaboration, publishing, and conversion—largely justifies the count.

Completeness3/5

The tool surface covers article lifecycle, review threads, publishing, export/conversion, and presence well, covering most common workflows. A notable gap is the lack of any delete_article or trash operation, leaving no way to remove documents. Additionally, updates are limited to anchored edits via edit_article, with no direct full-content replacement option.

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