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MCP Arabic Toolkit

MCP Arabic Toolkit

by seotarek
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

arabic_normalize_text

Normalize Arabic text by unifying Alef and Yaa variants, removing Tatweel, and stripping diacritics to produce consistent, searchable text.

Instructions

Normalizes Arabic text by unifying character variants (Alef, Yaa), removing Tatweel, and stripping diacritics.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "arabic_normalize_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe raw Arabic text to normalize.
unify_taaNoUnify Taa Marbuta (ة) into ه.
unify_yaaNoUnify final Yaa (ى) into ي.
unify_alefNoUnify all Alef variants (أ, إ, آ, ٱ) into ا.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full load, and it does disclose the observable transformations performed. But it hides a meaningful behavioral detail: Tatweel removal and diacritic stripping are unconditional (no parameter disables them), while variant unification is flag-controlled — a distinction the description flattens into a single undifferentiated list.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler; the verb and the transformation list arrive immediately. Nothing extraneous is included.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a four-parameter pure-transform function the schema does most of the work, but with no output schema and no annotations the description should say what comes back (a normalized string) and clarify that the unmentioned Tatweel/diacritic steps are always applied. The Taa Marbuta omission leaves the operation set inconsistent with the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented with its effect and default, giving a baseline of 3. The description adds no information beyond the schema and in fact under-reports it by omitting the unify_taa option entirely.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ("Normalizes") and resource ("Arabic text") and enumerates the concrete transformations: variant unification, Tatweel removal, diacritic stripping. However it names only Alef and Yaa as unified variants, omitting Taa Marbuta which the schema exposes, and it does not distinguish itself from the sibling arabic_strip_diacritics whose function it partially duplicates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to choose this tool over arabic_strip_diacritics, which strips diacritics — an operation this description explicitly claims as part of its own behavior. No prerequisites, no exclusions, and no mention of when the alternative would be preferable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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