tabella

A portable document lifecycle suite: create, convert, and read documents from structured JSON or raw HTML/CSS.

Cross-Platform Compatibility

This package conforms to the Agent Plugins v1.0.0 specification. It works with any compatible AI agent client that supports MCP servers, including:

• Hermes Agent
• Cursor
• Claude Desktop
• Any client that supports the Model Context Protocol

Three installation paths

Transports: the plugin ships both.

• stdio (default) — the declaring client spawns mcp_server.py itself (Agent Plugins v1 lifecycle; PLUGIN_ROOT/PLUGIN_DATA injected by the client). Nothing to run by hand.

• streamable-HTTP (self-host lane) — for clients that prefer connecting to a URL, run the server yourself:

  python3 scripts/mcp_http_server.py   # serves 127.0.0.1:8602
  

  First run bootstraps the dependency venv (or scripts/setup.sh); TABELLA_TOKEN enables bearer auth. Then point an mcp.json streamable-http entry at http://127.0.0.1:8602/mcp.

Agent Plugins v1 package — clients that support the v1 spec auto-discover plugin.json + mcp.json from the repo root and launch the MCP server:

# Hermes Agent
hermes plugins install dyiapanis/tabella --no-enable
hermes plugins enable tabella

# Other v1-compatible clients: follow your client's plugin installation instructions

MCP server (manual) — point any MCP-compatible client at the server directly:

{
  "mcpServers": {
    "tabella": {
      "type": "stdio",
      "command": "python3",
      "args": ["/path/to/tabella/mcp_server.py"]
    }
  }
}

pip install — use the Python library directly:

pip install tabella-docs[all]

Or install only the modules you need:

pip install tabella-docs[create]    # python-docx, openpyxl, python-pptx, weasyprint, odfpy
pip install tabella-docs[read]      # firecrawl-anydoc, liteparse, PyMuPDF
pip install tabella-docs[convert]   # no python deps (needs system pandoc; a TeX engine only for pdf output)

Modules

Create

Generate documents from structured JSON content or raw HTML/CSS:

• DOCX — Word documents (python-docx)
• XLSX — Spreadsheets (openpyxl)
• PPTX — Presentations (python-pptx)
• PDF — Print-ready documents and visual designs (WeasyPrint)
  • Document-style: structured JSON → HTML → PDF
  • Visual-style: raw HTML/CSS (Grid, Flexbox, gradients, themes) → PDF
• ODT/ODS/ODP — OpenDocument formats (odfpy)

Accepts plain strings (auto-normalized), HTML, or structured dicts with sections, sheets, slides, tables, images, and page breaks.

Convert

Format conversion via pandoc + LaTeX (page size, margins, headers/footers configurable). Pandoc 3.1.11 reports 44 input and 65 output formats — doc_convert passes your from/to through, so all of them work. (The counts move between pandoc minors; run pandoc --list-input-formats to check yours.) The everyday set:

Format	In	Out	Notes
Markdown (.md, variants)	✅	✅	gfm, commonmark, multimarkdown, strict…
HTML	✅	✅	html4/html5, chunkedhtml
DOCX (Word)	✅	✅
ODT	✅	✅	+ opendocument
PDF	✅	✅	in: pandoc cannot read PDF, so the text layer is extracted first (OCR when the page is a scan) and the result is converted; out: needs pdflatex/xelatex (texlive) — engine auto-selected
LaTeX (.tex)	✅	✅	incl. beamer slides out
RST	✅	✅
EPUB	✅	✅	out: epub2/epub3
CSV / TSV	✅	✅	out via markdown/AST route
RTF	✅	✅
Plain text (.txt)	✅	✅	read as markdown — pandoc has no plain reader, so headings and lists in a .txt survive
Org-mode, MediaWiki, Jira wiki, Textile, DocBook, FB2, OPML, IPynb, JATS	✅	✅	writers for most
Typst / man / ms / texinfo / AsciiDoc	✅	✅	typst in+out
Presentations	—	✅	beamer, revealjs, slidy, s5, dzslides (from any reader, Markdown especially)
Pandoc AST (JSON)	✅	✅	for programmatic pipelines

Everything not in the table: if pandoc --list-input-formats / --list-output-formats lists it, doc_convert accepts it as-is — unknown extensions pass through untouched.

Read

Extract text and metadata from any document:

• Office formats (DOCX, DOC, XLSX, XLSB, PPTX, PPT, ODT, ODS, ODP, RTF, EPUB, CSV) and text-based PDFs → GitHub-Flavored Markdown via anydoc (~4ms, in-process)
• Scanned PDFs and images (JPG, PNG, TIFF) → OCR via LiteParse (Tesseract)
• Plain text (.txt, .md, .json, .py, .yaml, etc.) → direct file read
• Returns structured Markdown (office formats) or plain text (OCR, plain text)

Prerequisites

Create module

The create module uses a separate Python venv for heavy document libraries. Set DOCS_VENV_PYTHON to point to it:

# Create a dedicated venv
python -m venv ~/.venvs/docs/venv
~/.venvs/docs/venv/bin/pip install python-docx openpyxl python-pptx weasyprint odfpy Pillow lxml PyMuPDF firecrawl-anydoc liteparse

Or install the libraries into your main Python environment:

pip install tabella-docs[create]

Read module

~/.venvs/docs/venv/bin/pip install firecrawl-anydoc liteparse PyMuPDF

Or:

pip install tabella-docs[read]

Convert module

pip install tabella-docs[convert]

Requires system-installed pandoc and a LaTeX engine:

# Debian/Ubuntu
sudo apt install pandoc texlive-xetex

# macOS
brew install pandoc basictex

# Arch
sudo pacman -S pandoc texlive

WeasyPrint system dependencies (PDF creation)

# Debian/Ubuntu
sudo apt install libcairo2 libpango-1.0-0 libgdk-pixbuf-2.0-0

# macOS
brew install cairo pango gdk-pixbuf

Windows

tabella runs natively on Windows 10/11 (64-bit, Python 3.11+); all five MCP tools are exercised on windows-latest in CI. Native dependencies, installed per feature:

• PDF creation (WeasyPrint) — the GTK3 runtime installer (its default install location is on WeasyPrint's search path)
• Convert — pandoc; a TeX engine (e.g. MiKTeX) only when the output format is PDF
• OCR fallback — Tesseract for Windows, only for scanned PDFs and images

The first-run dependency venv bootstraps natively on Windows (stdlib venv module — no bash, MSYS2, or WSL required).

Installation from source

git clone https://github.com/dyiapanis/tabella.git
cd tabella
pip install -e ".[all]"

Configuration

Variable	Purpose	Default
DOCS_VENV_PYTHON	Python binary for document libraries	PLUGIN_DATA/venv/bin/python or ~/.venvs/docs/venv/bin/python
DOCS_OUTPUT_DIR	Default output directory for generated documents	~/Documents
PLUGIN_DATA	Client-managed data directory (set by v1 client)	—
PLUGIN_ROOT	Plugin root directory (set by v1 client)	—

Automatic bootstrap

On first startup, the MCP server checks if the docs venv exists. If not, it creates one in PLUGIN_DATA (provided by the v1 client) or ~/.venvs/docs as fallback, and installs all dependencies: python-docx, openpyxl, python-pptx, weasyprint, odfpy, PyMuPDF, firecrawl-anydoc, liteparse. On POSIX this runs scripts/setup.sh; on Windows the venv is built with the standard library's venv module.

This means the v1 package is fully self-contained — no manual pip install required. The bootstrap runs once; subsequent startups use the existing venv.

Usage

Create a PDF from HTML/CSS

from tabella.create import create_document

result = create_document(
    format="pdf",
    content={"html": "<html><body><h1>Hello</h1></body></html>"},
    output_path="/tmp/output.pdf",
    options={"page_size": "A4"}
)

Read a DOCX file

from tabella.read import read

text = read("/path/to/document.docx")
# Returns GitHub-Flavored Markdown

Convert Markdown to PDF

from tabella.convert import doc_convert

result = doc_convert(
    input_path="/path/to/input.md",
    to_format="pdf",
    output_path="/path/to/output.pdf"
)

Via MCP

The bundled MCP server exposes five tools: doc_read, doc_read_meta, doc_convert, doc_create_document, doc_edit_pdf. Any MCP-compatible client can call these directly.

License

MIT