nativ-mcp

Official

by Nativ-Technologies

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Python

Remote

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TDQS

A4/5.0

Scored 2026-04-17 21:15 across 8 tools

Disambiguation5/5

Each tool has a clearly distinct purpose within the localization domain: add_translation_memory_entry stores translations, get_brand_voice retrieves brand guidelines, get_languages and get_style_guides fetch configuration, get_translation_memory_stats provides metrics, search_translation_memory finds existing translations, translate handles single translations, and translate_batch processes multiple texts. There is no overlap in functionality, making tool selection unambiguous.

Naming Consistency5/5

All tools follow a consistent verb_noun naming pattern (e.g., add_translation_memory_entry, get_brand_voice, search_translation_memory). The verbs are descriptive and appropriate for each action (add, get, search, translate), and snake_case is used uniformly throughout, creating a predictable and readable naming convention.

Tool Count5/5

With 8 tools, the server is well-scoped for localization tasks, covering core operations like translation, memory management, and configuration retrieval. Each tool serves a specific and necessary function without redundancy, making the count appropriate for the domain and avoiding both bloat and insufficiency.

Completeness4/5

The tool set provides comprehensive coverage for AI-driven localization, including translation (single and batch), translation memory management (add, search, stats), and configuration access (brand voice, languages, style guides). A minor gap exists in the lack of update or delete operations for translation memory entries or style guides, but agents can still perform essential workflows effectively.