Arabic Toolkit

A small Model Context Protocol (MCP) server exposing practical Arabic text utilities. Built with the official mcp Python SDK (FastMCP).

Demonstrates: MCP server authoring / tool development

All tools are implemented for real -- deterministic string processing plus one clearly-labelled heuristic. The pure logic lives in arabic_tools.py (no mcp dependency), so it is independently unit-tested; server.py is a thin MCP wrapper.

Tools

Tool	Description	Example input	Example output
normalise_arabic	NFC-normalises, removes diacritics (harakat/tashkil) and tatweel, and optionally unifies letter variants (alef/yeh/teh-marbuta).	الْعَرَبِيَّةُ	العربية
strip_tashkeel	Removes only the diacritics (and, by default, the tatweel); leaves letters as-is.	كــــتاب	كتاب
transliterate	Documented, deterministic Arabic→Latin romanisation (simplified DIN 31635 / ALA-LC, ASCII digraphs).	كَتَبَ	{"transliteration": "kataba", "scheme": "din31635-simplified-ascii"}
detect_dialect	Heuristic dialect guess (Egyptian/Levantine/Gulf/Maghrebi/MSA) from marker words. Not a trained classifier — see limits below.	شو بدك هلق؟	{"dialect": "levantine", "confidence": 1.0, ...}
count_tokens	Whitespace-token count plus character and Arabic-character statistics.	مرحبا يا عالم	{"tokens": 3, "characters": 13, ...}

About detect_dialect (read this)

detect_dialect is an honest heuristic, not a machine-learning model. It counts hand-picked marker words/particles per dialect and returns the highest scorer. Known limits:

• Only five coarse groups (Egyptian, Levantine, Gulf, Maghrebi, MSA).
• Unreliable on short input, mixed-dialect text, and code-switching.
• confidence is a crude ratio (winning hits / total hits), not a calibrated probability.
• Falls back to MSA with confidence: 0.0 when no markers are found.

For production-grade detection, train a supervised classifier (e.g. fastText or a fine-tuned transformer) on a labelled corpus such as MADAR or NADI.

About transliterate

The romanisation is deterministic and documented but intentionally simple:

• No vowel inference — short vowels are produced only from explicit harakat.
• No context-sensitive rules — the article ال is always al- (no sun-letter assimilation), and hamzat al-wasl is not elided.
• Shadda doubles the preceding consonant; sukun emits no vowel.
• One-way (Arabic → Latin); not round-trippable.

Install

Requires Python 3.10+.

# Clone, then install the package (editable for local development):
pip install -e .

This pulls in the mcp SDK and registers a mcp-arabic-toolkit console script.

The tests themselves need only pytest (no mcp SDK):

pip install pytest

Run

# Option A: run the module directly (stdio transport)
python server.py

# Option B: run the installed console script
mcp-arabic-toolkit

Register with an MCP client

To use it from Claude Desktop (or any MCP client), add an entry to the client's MCP server config:

{
  "mcpServers": {
    "arabic-toolkit": {
      "command": "python",
      "args": ["/absolute/path/to/mcp-arabic-toolkit/server.py"]
    }
  }
}

Test

python -m pytest tests/ -v

The suite (tests/test_tools.py) imports the pure logic directly and covers every tool with concrete examples (diacritic/tatweel removal, letter unification, transliteration with and without harakat, each dialect, and token counting).

Quick local check

python -c "import arabic_tools; print(arabic_tools.normalise_arabic('الْعَرَبِيَّةُ'))"
# -> العربية

Publishing to the MCP registry

This package ships a server.json manifest compatible with the official MCP registry.

Exact metadata (server.json)

{
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-07-09/server.schema.json",
  "name": "io.github.benjiscollector/mcp-arabic-toolkit",
  "description": "MCP server exposing Arabic text utilities: normalisation, tashkeel stripping, transliteration, a heuristic dialect detector, and token counting.",
  "status": "active",
  "repository": {
    "url": "https://github.com/BenjisCollector/mcp-arabic-toolkit",
    "source": "github"
  },
  "version": "0.2.0",
  "packages": [
    {
      "registryType": "pypi",
      "registryBaseUrl": "https://pypi.org",
      "identifier": "mcp-arabic-toolkit",
      "version": "0.2.0",
      "transport": { "type": "stdio" }
    }
  ]
}

The server name uses the io.github.<owner>/<repo> namespace, which the registry verifies against GitHub ownership during publish.

Steps

1. Build and publish the PyPI package so the registry has something to point at:

   python -m build
   twine upload dist/*
   

2. Install the registry publisher CLI (mcp-publisher) — see the registry publishing guide.
3. Authenticate with GitHub so the CLI can verify the io.github.* namespace:

   mcp-publisher login github
   

4. Publish from the directory containing server.json:

   mcp-publisher publish
   

To list this server on the community modelcontextprotocol/servers README as well, see SUBMISSION.md for the exact entry text and PR steps.

License

MIT — see LICENSE.