arabic-nlp-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| arabic_normalizeC | Normalize Arabic text with individually controlled Unicode rules. |
| arabic_prepare_for_searchA | Prepare Arabic/mixed text for search, RAG, and deduplication. Returns normalized tokens, a stable SHA-256 fingerprint, lossiness, warnings, and the exact transformation audit trail. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have distinct purposes: one performs general normalization with controllable Unicode rules, while the other is specifically for preparing text for search/RAG/deduplication with richer output. There is some conceptual overlap since both involve normalization, but the names and descriptions clearly differentiate the more fundamental operation from the higher-level preparation pipeline.
Both tool names follow a consistent 'arabic_<action>' snake_case pattern, with a clear verb indicating the operation. The prefix 'arabic_' also makes the domain obvious, and the naming style is uniform across the set.
With only two tools, the server feels thin for a general Arabic NLP package. However, the two tools form a coherent pair that could be sufficient for a narrowly focused normalization and search-preparation use case.
The tools cover a normalization-to-search-preparation workflow with outputs like tokens, fingerprints, and audit trails, which addresses common RAG/deduplication needs. Missing are other typical Arabic NLP operations such as stemming, transliteration, named-entity recognition, or morphological analysis, leaving the server somewhat incomplete for a broader NLP scope.