Skip to main content
Glama
Sign In
MCP Arabic Toolkit

MCP Arabic Toolkit

by seotarek
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

arabic_detect_dialect

Classifies Arabic text as Modern Standard Arabic, Egyptian, Levantine, Gulf, or Maghrebi to identify regional dialect for language analysis and processing.

Instructions

Classifies Arabic text into Modern Standard Arabic (MSA), Egyptian, Levantine, Gulf, or Maghrebi.

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_detect_dialect"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe Arabic text to analyze.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the exact label set returned by the classifier, but it does not mention confidence scores, handling of mixed-dialect or non-Arabic input, or determinism, leaving important behavioral questions unanswered.

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?

The definition is a single front-loaded sentence with no filler. Every phrase contributes either the classification action, the input domain, or the output categories.

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

Completeness4/5

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

For a simple one-parameter classifier with no output schema, the description provides enough to invoke it and understand the returned labels. It could be slightly more complete by stating edge-case behavior, but the essential context is present.

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%, and the single parameter is already documented as 'The Arabic text to analyze.' The description adds no syntax, format, length, or encoding details beyond the schema, so the baseline of 3 is appropriate.

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

Purpose5/5

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

The description states a specific verb, 'Classifies', and a specific resource, 'Arabic text', then enumerates the exact output categories MSA, Egyptian, Levantine, Gulf, and Maghrebi. This clearly distinguishes it from sibling tools that normalize, strip diacritics, or compute token metrics.

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

Usage Guidelines3/5

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

The purpose implies when the tool is useful, but the description gives no explicit guidance on when to choose it over sibling tools or any prerequisites. It does not mention, for example, whether the text should be normalized first or how non-Arabic or mixed-dialect input is handled.

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

Deploy Server

Other Tools