Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
作者提供的说明(英文原文)
Split Markdown into chunks by heading. Each chunk keeps its heading path and line numbers.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"md-chunk-lite": {
"command": "uvx",
"args": [
"md-chunk-lite",
"mcp"
]
}
}
}在终端中运行这条命令
claude mcp add md-chunk-lite -- uvx md-chunk-lite mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"md-chunk-lite": {
"command": "uvx",
"args": [
"md-chunk-lite",
"mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"md-chunk-lite": {
"type": "stdio",
"command": "uvx",
"args": [
"md-chunk-lite",
"mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 tidybytetools 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.0.0)于3天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
同一分类中有:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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.