A
licenseB
qualityD
maintenanceEnables AI applications to perform multi-language translations with automatic language detection, contextual hints, and custom instructions through an MCP-compatible interface.
1
1,468 npm
MIT
nativ-mcp
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TDQS
A3.7/5.0
Scored across 21 tools
Disambiguation4/5
Most tools target clearly distinct resources and actions, such as translate vs translate_batch or extract_image_text vs localize_image. Slight overlap exists between parse_subtitle and parse_audio_script, since both can handle .srt/.itt inputs, but their descriptions clarify the intended source type.
Naming Consistency5/5
Tool names follow a consistent snake_case verb_noun pattern throughout, e.g. list_voices, preview_voice, register_video, and translate_batch. There are no mixed conventions or vague action verbs.
Tool Count4/5
At 21 tools, the server is above the ideal 3-15 range, but the count is justified by the breadth of the localization domain: text translation, translation memory, style/brand config, subtitles, images, and audio/video dubbing. The tools do not feel redundant, though the set is on the heavier side.
Completeness4/5
Core localization workflows are well covered: text translation, batch translation, subtitle localization, image localization, transcription, TTS dubbing, and cultural checks. Minor gaps exist, such as no update/delete/disable for translation memory entries and no style guide or brand voice management beyond read-only access.
Maintenance
ActivityMaintained
ResponsivenessNo issues