Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
作者による説明(英語の原文)
A PDF's real sections for agents: heading tree, pages and section text. Free mode is local.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
OPENAI_API_KEY, ANTHROPIC_API_KEY「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"section-anything": {
"command": "uvx",
"args": [
"section-anything-mcp"
],
"env": {
"OPENAI_API_KEY": "your-value",
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add section-anything --env OPENAI_API_KEY=your-value --env ANTHROPIC_API_KEY=your-value -- uvx section-anything-mcp動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"section-anything": {
"command": "uvx",
"args": [
"section-anything-mcp"
],
"env": {
"OPENAI_API_KEY": "your-value",
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"section-anything": {
"type": "stdio",
"command": "uvx",
"args": [
"section-anything-mcp"
],
"env": {
"OPENAI_API_KEY": "your-value",
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント Omniwot で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
入力が必要なもの:OPENAI_API_KEY, ANTHROPIC_API_KEY
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.1.0)がレジストリに届いたのは4 日前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにPython (uv)も必要です。
同じカテゴリには:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日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.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
Find the real sections of a PDF: every heading, its level, the pages it spans and the exact text under it. Built for RAG pipelines and document tools that need structure, with a dial from free rules to an LLM that reads the whole document, and a hard cost cap on every paid run.
Real output of section-anything 0.1.3, Quick mode (gpt-6-luna), on *Visual Instruction Tuning*, Liu et al., arXiv:2304.08485, CC BY 4.0; the wait for the run is shortened (capture, script). Quick mode's output can vary slightly between runs. Try it on that paper in the Colab notebook (Free mode, no key needed), or in the browser on the Hugging Face Space (Free mode on any PDF, plus saved Quick check and Standard runs on three example papers; don't upload confidential documents).
Pre-release (0.1). The API may change before 1.0.
Website: https://omniwot.github.io/section-anything/ (how it works, benchmarks, examples)
import section_anything
tree = section_anything.section("paper.pdf", mode="quick") print(tree.to_markdown())
print(tree.summary()) # mode, model, pages, sections, calls, tokens, cost_usd, seconds
An agent can take a PDF's outline first and then read only the sections it needs (0.1.6 or newer).
MCP server for Claude Code, Claude Desktop, Cursor and other MCP hosts, with three read-only tools: section_pdf (the heading tree; free mode by default, no model, $0), read_sections (the text of chosen sections) and estimate_cost (what quick and standard would cost).
claude mcp add --transport stdio section-anything -- uvx --from "section-anything[mcp]" section-anything-mcp
Other hosts take the same command in their mcpServers JSON:
{"mcpServers": {"section-anything": {"command": "uvx",
"args": ["--from", "section-anything[mcp]", "section-anything-mcp", "--max-usd", "0"]}}}
Paid modes use the API keys in the server's environment (claude mcp add --env OPENAI_API_KEY=...) and are capped per document by --max-usd (default 0.50; 0 keeps every run free). --root DIR limits the folders the server may read.
Agent skill: copy skills/section-anything into your agent's skills folder (Claude Code: ~/.claude/skills/) and the agent uses the command line the same way: outline, then the sections it needs.
Shell: section-anything paper.pdf --format json --body prints every section with its heading_path, parent, pages and text.
PDF converters mostly infer headings from how a line looks, so paper titles, author names, captions and theorem statements can come out as sections, and real subsections can get the wrong level. Anything that chunks or navigates by headings inherits those mistakes. Section Anything combines the PDF's own outline (bookmarks) and printed table of contents, typography and numbering rules, and optionally a model that reviews or reads the text.
Heading-tree similarity (TEDS, 0–100, higher is better) on READoc with READoc's own scoring code, October 2026. Docling and MinerU convert whole documents (layout, tables, figures, formulas, OCR); these numbers compare only the heading tree. Papers not used while developing the method:
| READoc arXiv papers (held out) | Docs | TEDS |
|---|---|---|
Section Anything standard (gpt-6-luna) | 905 | 70.0 |
Section Anything quick (gpt-6-luna) | 905 | 68.0 |
Section Anything free (no model) | 905 | 60.0 |
| Docling 2.140 (heading hierarchy on) | 905 | 49.4 |
quick is release 0.1.2 and free release 0.1.5. 0.1.1 found the abstract's heading (journals often set it small, bold or run into the first line): +2.4 to free and +3.7 to quick in a pre-registered held-out test (0.1.0: 54.8 and 63.9). 0.1.2 keeps a section number that the PDF extractor split off its title ("1" | "Introduction"): +0.7 to free (95% CI +0.5 to +1.0) and +0.5 to quick (95% CI +0.4 to +0.8; +1.7 on the 291 papers it changes) in a second pre-registered test (0.1.1: 57.2 and 67.4). 0.1.5 gives back matter (References, Acknowledgements, Appendix) and appendix sections the level of the numbered sections: +2.1 to free (95% CI +1.7 to +2.4; 136 papers better, 4 worse) in a fourth pre-registered test on these papers (0.1.4: 57.9); READMEs unchanged. standard uses none of these. See the changelog.
On the same 905 papers with MinerU's cloud (3.4.4, VLM): our standard 70.0, our quick 68.0, MinerU + its own LLM title levels (gpt-6.1-sol) 64.8 (paired difference: standard +5.1, 95% CI +4.0 to +6.2, or +2.8 without the 154 papers where that step, run by us on gpt-6.1-sol, returned no parsable levels and kept MinerU's raw levels; quick +3.1, 95% CI +2.1 to +4.1, but level without those papers: +1.0, 95% CI −0.03 to +2.0), MinerU alone 53.9, Docling 49.4.
On GitHub READMEs, free scores higher than Docling: on 1,112 bookmarked READoc READMEs not used in development, 81.9 against 75.5, and Docling reaches 79.7 with the opt-in outline check we proposed upstream (docling#5002), the same idea as free's outline check. On 27 UK government reports never used in development (answer keys from each report's HTML version), the outline check improved free by +5.7 (95% CI +0.4 to +12.2; 43.1 → 48.8; measured on 0.1.0).
Where others win: on the 1,124 held-out READoc READMEs our quick and standard only tie with Docling (about 75). On the 905 held-out papers Docling scored higher than standard on 103 papers and lower on 773 (by more than 0.5 points). Nougat, an OCR model trained on arXiv, has the highest published READoc arXiv score (88.5 in the READoc paper, older tool versions; we have not run it). Docling and MinerU also read scanned pages with OCR; Section Anything needs a text layer. Better headings improved RAG retrieval in our Qasper test but not answer quality.
| Mode | What runs | Sent to a model | Cost per 1,000 pages |
|---|---|---|---|
free (default) | bookmarks or the printed contents as the outline, then typography and numbering rules; headings a complete-looking outline does not list are dropped | nothing | $0 |
quick | rules propose candidate heading lines; a small model keeps the real ones and sets levels | candidate lines only | about $0.07 (gpt-6-luna) |
standard | a model reads every line, with font hints, and marks where sections start and end | the document text | about $0.16 (gpt-6-luna, the default); $2.90 with gpt-6.1-sol |
local: | quick, answered by a local Ollama model | nothing leaves the machine | $0 |
Every paid run is estimated first and capped (default $0.50 per document); over the cap it downgrades (standard → quick → free) or fails, as you choose.
The default model for quick and standard is gpt-6-luna, the model behind their READoc scores above. standard can run on gpt-6.1-sol instead (SECTIONANY_STANDARD_MODEL=gpt-6.1-sol or model="gpt-6.1-sol") at about 18 times the cost. On development documents only (98 READoc papers and 100 READMEs, same code) it scored 5.8 points higher on READMEs (95% CI +2.1 to +9.6) and 0.9 higher on papers (CI -0.3 to +2.1, not significant); this is not confirmed on held-out documents.
Python 3.11 or newer:
pip install section-anything
pip install "section-anything[anthropic]" # optional: Claude models on Anthropic's own API pip install "section-anything[docling]" # optional: tree.to_docling_document() pip install "section-anything[langfuse]" # optional Langfuse tracing pip install "section-anything[mcp]" # optional MCP server: section-anything-mcp
pip install "git+https://github.com/Omniwot/section-anything#subdirectory=packages/section-anything"
Command line:
section-anything paper.pdf # free, Markdown headings
section-anything paper.pdf --mode quick --format json # JSON with spans and cost section-anything paper.pdf --mode standard --max-usd 0.5 # cap this document at $0.50
API keys come from the environment only (SECTIONANY_API_KEY, or OPENAI_API_KEY for OpenAI's endpoint); any OpenAI-compatible endpoint works. Claude models run on Anthropic's own API with the anthropic extra and ANTHROPIC_API_KEY; on development documents Claude Haiku 5.5 tied the default at about 3 times the cost (Providers). Full options, output format and the security model for untrusted PDFs: packages/section-anything/README.md.
One document per section, with the heading path, pages and character span as metadata. Each package depends only on section-anything and the framework's core package.
pip install langchain-section-anything
pip install llama-index-readers-section-anything pip install lightrag-section-anything
| Framework | Package | Use |
|---|---|---|
| LangChain | langchain-section-anything | SectionAnythingLoader("paper.pdf", mode="free").load() |
| LlamaIndex | llama-index-readers-section-anything | SectionAnythingReader(mode="free").load_data("paper.pdf"), or as the .pdf reader of SimpleDirectoryReader |
| LightRAG | lightrag-section-anything | parser engines (sectionany_free, sectionany_quick, ...) for LightRAG's heading-based chunker, without a MinerU or Docling service |
To run Docling's HybridChunker or Unstructured's chunk_by_title on these headings, export the tree with tree.to_docling_document() or tree.to_unstructured_elements() (how).
free is best when the PDF has bookmarks or a printed table of contents. When the bookmarks look complete, it drops headings they do not list; if the bookmarks stop at the top levels, real deeper headings are lost (about 6% of READMEs lose more than 5 points). Turn this off with SECTIONANY_DEMOTE_UNLISTED_HEADINGS='[]'.quick and standard send text to the provider you configure.| Path | What it is |
|---|
| packages/section-anything/ | the library and CLI (Apache-2.0)
冒頭だけを表示しています。続きはリポジトリで読めます