This tutorial shows a practical workflow for converting those files into Markdown before passing the result to an AI agent.

Why normalize files to Markdown?

Raw documents contain layout instructions, embedded objects, headers, and formatting noise. Sending them directly into a retrieval or agent workflow can produce inconsistent chunks and make debugging difficult.

Markdown gives you:

• human-readable output that can be reviewed before use
• predictable headings and lists for chunking
• a portable format for Git, documentation, RAG, and agent pipelines
• less dependence on a single model or framework

The workflow

A simple document-processing pipeline looks like this:

1. Receive a file from a user or an approved source.
2. Convert it to Markdown.
3. Review or validate the output.
4. Split the Markdown into meaningful sections.
5. Send only the relevant sections to the model.

Markovo handles the conversion step through its web interface, API, CLI, or MCP integration. It supports common document formats as well as images, audio, and video.

Use Markovo through MCP

MCP, or Model Context Protocol, gives compatible AI clients a standard way to call external tools. Instead of manually converting a file and copying the result, an agent can request a conversion as part of its workflow.

A typical instruction might be:

Convert this authorized document to Markdown, preserve its heading structure,
and return the result for review before it is used as model context.

The important part is the review step. Conversion should not automatically mean ingestion. Check the Markdown for missing tables, incorrect headings, or content that should not be shared with the model.

API and CLI workflows

For repeatable backend jobs, use the API or CLI instead of browser automation. A production workflow should:

• keep credentials in environment variables
• enforce file-size and format limits
• log conversion failures without storing private document contents
• retry only safe, idempotent operations
• require authorization before fetching remote URLs

You can find implementation patterns in the Markovo examples repository.

A practical RAG pattern

After conversion, split the Markdown by semantic boundaries such as headings rather than fixed character counts. Store the source name and heading path as metadata with each chunk.

For example:

{
  "source": "product-manual.pdf",
  "section": "Installation > Requirements",
  "content": "..."
}

This makes retrieval results easier to trace and lets the application show users where an answer came from.

When to use the web app, API, CLI, or MCP

• Web app: one-off conversions and manual review
• API: server-side applications and batch workflows
• CLI: local automation and developer scripts
• MCP: agent-driven workflows inside compatible AI clients

The best interface depends on who initiates the conversion and how much control the workflow needs.

Final thoughts

Document conversion is not only a formatting task. It is a reliability boundary between messy source files and the AI system that consumes them. Clean Markdown makes that boundary visible and testable.

Try the converter at markovo.net, or explore the developer examples on GitHub.