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md-chunk-lite

by tidybytetoolsversion 1.0.0i★ –iRuns on your computer
Registry nameio.github.tidybytetools/md-chunk-lite

Install

Overview

Description from the maker (original English)

Split Markdown into chunks by heading. Each chunk keeps its heading path and line numbers.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Docs, sheets & office work

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "md-chunk-lite": {
      "command": "uvx",
      "args": [
        "md-chunk-lite",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add md-chunk-lite -- uvx md-chunk-lite mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "md-chunk-lite": {
      "command": "uvx",
      "args": [
        "md-chunk-lite",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "md-chunk-lite": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "md-chunk-lite",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account tidybytetools, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Docs, sheets & office worki

  • More stars than 0% of the other 3532 servers in this category
  • Published to the registry more recently than 89% of the other 3532 servers in this category
  • Basic checks equal to or better than 100% of the other 3532 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is md-chunk-lite safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 3 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Agent-Native Google Calendar - manage events, sync, and public booking

Runs in the cloud★ 7,620MITUpdated 2 months ago
Install

Excel MCP Server

by haris-musa

Lets AI create and edit Excel files itself, including formulas, charts and PivotTables, without Excel installed.

Runs on your computer★ 4,276MITUpdated 7 days ago
Install

Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

Runs on your computer★ 847MITUpdated 4 days ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

md-chunk-lite

Free tool that splits Markdown into chunks by heading. Every chunk knows its heading path and the line numbers it came from. Need PDF, Word or web page files turned into chunks (with page numbers for PDFs), or whole folders? The paid doc2md does that: [doc2md on Gumroad](https://tidybyte.gumroad.com/l/frlkv)

Good for AI search (RAG), embeddings, and docs bots. Plain code, not AI, so the same file always gives the same chunks. Runs on your own computer. MIT license. Python 3.10 or newer, on Windows, Mac and Linux.

Install

pip install md-chunk-lite

Or use pipx, which keeps it in its own space:

pipx install md-chunk-lite

Want the newest code from GitHub instead?

pip install git+https://github.com/tidybytetools/md-chunk-lite

Use it

md-chunk-lite guide.md                     # writes guide.chunks.jsonl

md-chunk-lite docs/*.md -o all.jsonl # many files into one file md-chunk-lite guide.md -o - # print the chunks on screen md-chunk-lite guide.md --max-chars 800 # smaller chunks md-chunk-lite guide.md --outline # just show the headings cat guide.md | md-chunk-lite - # read from stdin

Example run:

Saved: guide.chunks.jsonl  (7 chunks, biggest 151 chars)

Each line of the .jsonl file is one chunk:

{

"id": "guide.md#3", "source": "guide.md", "index": 3, "heading_path": ["Garden Guide", "Planting", "Tomatoes"], "heading_level": 3, "anchor": "tomatoes", "start_line": 16, "end_line": 24, "part": 1, "parts": 1, "chars": 151, "text": "### Tomatoes\n\nPlant tomatoes after the last frost. Give them sun.\n\n`python\n..." }

  • heading_path: all the headings above this chunk, from the top.
  • anchor: the #link GitHub makes for that heading, so you can link straight to it.
  • start_line and end_line: where the chunk is in the file (the first line is 1).
  • part and parts: a big section is split into parts. part 2, parts 3 means the 2nd of 3.
  • chars: how long text is.

--outline prints the headings:

Garden Guide  [line 8, #garden-guide, 14 chars]

Tools [line 10, #tools, 47 chars] Planting [line 14, #planting, 11 chars] Tomatoes [line 16, #tomatoes, 151 chars]

When you run it in a terminal it ends with one short line about the paid doc2md. --quiet turns it off, and it never shows when output is piped or redirected.

How it splits

  1. Each heading starts a new section. A chunk never mixes two sections, so every chunk has one clear heading path.
  2. A small section becomes one chunk. Its text is copied straight from your file, so the line numbers match.
  3. A big section is split at blank lines. Lists, tables and code blocks stay whole when they fit.
  4. A table that is too big is split by rows, and each part gets the header row again.
  5. A code block that is too big is split by lines, and each part gets its own ` fence again, with the same language.
  6. A very long line is cut at the end of a sentence, then at a space if it has to be.
  7. A heading with no text of its own (just more headings under it) does not get its own chunk, but it still shows in the heading_path of the chunks under it.

It knows # headings and underlined headings (=== and ---). It skips headings inside code blocks and YAML front matter (the --- block at the very top).

Options:

  • --max-chars N: biggest chunk, in characters (default 1500, smallest 200). About 4 characters make 1 token in English.
  • --overlap-chars N: when a section is split, start each part with up to N characters from the end of the part before it (default 0). It is never added inside a code block or table, and never between two different sections.
  • --prefix-path: start each chunk's text with its heading path, like Garden Guide > Planting > Tomatoes. This often helps search. It counts toward --max-chars.
  • --json: print one JSON object for scripts and AI agents instead of saving a file.

Known limits

  • Sizes are characters, not tokens. It does not load a tokenizer.
  • Markdown and plain text only. PDF, Word and HTML are not read. The paid doc2md turns those into chunks (with page numbers for PDFs).
  • No folders. Give it files (your shell can expand docs/*.md). The paid doc2md walks whole folders.
  • Not a full Markdown parser. A heading inside an HTML block, or an underlined heading that spans more than one line, may be handled wrong.
  • Overlap uses whole words from the part before, so it can be a little shorter than you asked for.

Lite vs paid

md-chunk-lite (free)doc2md (paid)
ReadsMarkdown and text filesPDF, Word (.docx) and HTML
Chunks with the heading pathyesyes
Line numbers and anchorsyesno (page numbers for PDFs instead)
Rough token count on each chunknoyes
Whole folders, sub-folders toonoyes
MCP serveryesno

Other good free tools

Pick what fits you. If you already use LangChain, its MarkdownHeaderTextSplitter also splits by heading. Chonkie and semchunk split any text by token count. md-chunk-lite is small, MIT, has no AI or tokenizer parts, gives you line numbers and anchors, and has an MCP server built in.

Use it from Claude Desktop or Cursor (MCP)

There is a small free MCP server built in. It needs uv.

Run it by hand to check it starts (it waits quietly for an app to talk to it):

uvx md-chunk-lite mcp

Claude Desktop (claude_desktop_config.json) or Cursor (~/.cursor/mcp.json):

{

"mcpServers": { "md-chunk-lite": { "command": "uvx", "args": ["md-chunk-lite", "mcp"] } } }

uvx --from md-chunk-lite md-chunk-lite-mcp starts the same server.

Tools (each one takes a file path or Markdown text):

ToolWhat it doesChanges files?
chunk_markdownSplits into chunks. Returns 100 at a time; use start for more.No
outline_markdownLists the headings with line numbers and anchors.No
get_sectionGets one section by heading or anchor.No
save_chunks_jsonlSaves the chunks to a .jsonl file.Writes one .jsonl file (replaces it if it is there)

None of them use the internet.

Use it in GitHub Actions

Chunk your docs on every push, so your AI search or docs bot always gets fresh chunks. It also warns you when a section is too big for one chunk, right on the line in the pull request.

name: Docs chunks

on: [push, pull_request] permissions: contents: read jobs: chunks: runs-on: ubuntu-latest steps:

  • uses: actions/checkout@v7
  • uses: tidybytetools/md-chunk-lite@v1 with: files: docs/**/*.md max-chars: "1500" artifact-name: docs-chunks # download the JSONL from the run page

Want a big section to fail the check instead of just warn? Add fail-on-split: "true".

It runs the code in the same tag you pick, with only Python's standard library. No API key, no account, and nothing is sent outside GitHub. It works on Linux, Windows and Mac runners.

Inputs
InputDefaultWhat it does
files**/*.mdWhich files to read. Glob patterns, one per line or split by commas.
excludenode_modules/**, **/node_modules/**, vendor/**Patterns to skip.
max-chars1500Biggest chunk, in characters (200 or more).
overlap-chars0Characters from the part before to repeat at the start of the next part.
prefix-pathfalseStart each chunk's text with its heading path.
outputmd-chunks.jsonlWhere to save the chunks.
fail-on-splitfalseFail when a section is too big for one chunk.
fail-if-no-filestrueFail when no files match.
artifact-name(empty)If set, upload the JSONL as an artifact with this name.
python-version(empty)Python to set up. Empty uses the runner's Python (3.10 or newer).
Outputs
OutputWhat it is
filesHow many files were read.
chunksHow many chunks were made.
biggestSize of the biggest chunk, in characters.
split-sectionsHow many sections were too big and got split.
outputPath of the JSONL file.

The run page also gets a short report: chunks per file, and a list of every section that got split, with its line number.

For AI agents

  • Use when: you have a Markdown or text file (docs, notes, a README) and need chunks with heading paths and line numbers for search, embeddings or citing sources. Use outline_markdown and get_section to read just one part of a long file.
  • Don't use when: the file is PDF, Word or HTML (convert it first), or you need token-exact sizes.
  • Input: a file path or Markdown text, plus max_chars (default 1500), overlap_chars (default 0) and prefix_path (default false).
  • Output: chunks with id, source, index, heading_path, heading_level, anchor, start_line, end_line, part, parts, chars, text.
  • CLI exit code is 0 on success and 1 on error (message on stderr, or {"ok": false, "error": ...} with --json). See also llms.txt.

Python

from md_chunk_lite import chunk_file, chunk_markdown, outline, get_section

chunks = chunk_file("guide.md", max_chars=1000) for c in chunks: print(c["heading_path"], c["start_line"], c["end_line"])

install = get_section(open("README.md").read(), heading="Install")

The chunker file uses only Python's standard library. Installing with pip also adds the mcp package for the MCP server.

Tests

pip install "mcp>=1.10,<2"

python3 -m unittest discover -s tests

The MCP tests are skipped if mcp is not installed.

License

MIT. See LICENSE.

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