Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
Beschreibung vom Entwickler (englisches Original)
A PDF's real sections for agents: heading tree, pages and section text. Free mode is local.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
OPENAI_API_KEY, ANTHROPIC_API_KEYAls geheim markierte Werte sind Schlüssel zu deinem Konto. Gib sie nie weiter und füge sie nie in einen Chat ein.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"section-anything": {
"command": "uvx",
"args": [
"section-anything-mcp"
],
"env": {
"OPENAI_API_KEY": "your-value",
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add section-anything --env OPENAI_API_KEY=your-value --env ANTHROPIC_API_KEY=your-value -- uvx section-anything-mcpTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"section-anything": {
"command": "uvx",
"args": [
"section-anything-mcp"
],
"env": {
"OPENAI_API_KEY": "your-value",
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"section-anything": {
"type": "stdio",
"command": "uvx",
"args": [
"section-anything-mcp"
],
"env": {
"OPENAI_API_KEY": "your-value",
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto Omniwot bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Das musst du eintragen: OPENAI_API_KEY, ANTHROPIC_API_KEY
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.1.0) kam vor 4 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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)
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