Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
Description from the maker (original English)
Split Markdown into chunks by heading. Each chunk keeps its heading path and line numbers.
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We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
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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 mcpPaste 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.
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.
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Runs on your computer; the tool list appears in your AI app after you install it.
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
The registry name was verified with the GitHub account tidybytetools, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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.
The registry does not list a required key, though some servers ask you to sign in on first use.
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).
The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 3 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
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
Lets AI create and edit Excel files itself, including formulas, charts and PivotTables, without Excel installed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
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.
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.
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
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.
` fence again, with the same language.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.docs/*.md). The paid doc2md walks whole folders.| md-chunk-lite (free) | doc2md (paid) | |
|---|---|---|
| Reads | Markdown and text files | PDF, Word (.docx) and HTML |
| Chunks with the heading path | yes | yes |
| Line numbers and anchors | yes | no (page numbers for PDFs instead) |
| Rough token count on each chunk | no | yes |
| Whole folders, sub-folders too | no | yes |
| MCP server | yes | no |
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.
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):
| Tool | What it does | Changes files? |
|---|---|---|
chunk_markdown | Splits into chunks. Returns 100 at a time; use start for more. | No |
outline_markdown | Lists the headings with line numbers and anchors. | No |
get_section | Gets one section by heading or anchor. | No |
save_chunks_jsonl | Saves the chunks to a .jsonl file. | Writes one .jsonl file (replaces it if it is there) |
None of them use the internet.
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:
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.
| Input | Default | What it does |
|---|---|---|
files | **/*.md | Which files to read. Glob patterns, one per line or split by commas. |
exclude | node_modules/**, **/node_modules/**, vendor/** | Patterns to skip. |
max-chars | 1500 | Biggest chunk, in characters (200 or more). |
overlap-chars | 0 | Characters from the part before to repeat at the start of the next part. |
prefix-path | false | Start each chunk's text with its heading path. |
output | md-chunks.jsonl | Where to save the chunks. |
fail-on-split | false | Fail when a section is too big for one chunk. |
fail-if-no-files | true | Fail 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). |
| Output | What it is |
|---|---|
files | How many files were read. |
chunks | How many chunks were made. |
biggest | Size of the biggest chunk, in characters. |
split-sections | How many sections were too big and got split. |
output | Path 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.
outline_markdown and get_section to read just one part of a long file.max_chars (default 1500), overlap_chars (default 0) and prefix_path (default false).id, source, index, heading_path, heading_level, anchor, start_line, end_line, part, parts, chars, text.{"ok": false, "error": ...} with --json). See also llms.txt.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.
pip install "mcp>=1.10,<2"
python3 -m unittest discover -s tests
The MCP tests are skipped if mcp is not installed.
MIT. See LICENSE.