nativ-mcp
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NATIV_API_KEY | Yes | Your Nativ API key (nativ_xxx...) | |
| NATIV_API_URL | No | API base URL (defaults to https://api.usenativ.com) |
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 |
|---|---|
| translateA | Translate text using Nativ's AI localization engine. Uses the team's translation memory, style guides, and brand voice automatically. Returns the translation along with TM match info and rationale. Args: text: The text to translate. target_language: Full target language name (e.g. "French", "German", "Japanese"). target_language_code: Optional ISO language code (e.g. "fr", "de", "ja"). source_language: Source language name. Defaults to English. source_language_code: Source language code. Defaults to "en". context: Optional context to guide the translation (e.g. "marketing headline for Gen Z audience"). glossary: Optional inline glossary as CSV (e.g. "term,translation\nbrand,marque"). formality: Tone override — one of: very_informal, informal, neutral, formal, very_formal. max_characters: Optional strict character limit for the translation output. backtranslate: If true, also returns a back-translation to verify intent. |
| translate_batchA | Translate multiple texts to a single target language. Useful for localizing lists of strings, UI labels, or i18n files. Each text is translated individually using the team's TM and style guides. Args: texts: List of texts to translate. target_language: Full target language name (e.g. "French"). target_language_code: Optional ISO language code. source_language: Source language name. Defaults to English. source_language_code: Source language code. Defaults to "en". context: Optional context to guide all translations. formality: Tone override for all translations. |
| search_translation_memoryA | Search the translation memory for existing translations. Use this to check if translations already exist before creating new ones, or to find reference translations for consistency. Args: query: Text to search for in the translation memory. source_language_code: Source language code (default: "en"). target_language_code: Optional target language code to filter results. min_score: Minimum fuzzy match score (0-100). Default 0 returns all. limit: Maximum number of results (default 10). |
| add_translation_memory_entryA | Add a new entry to the translation memory. Use this to store approved translations so they are reused in future localizations. Args: source_text: The original text. target_text: The approved translation. source_language_code: Source language code (e.g. "en"). target_language_code: Target language code (e.g. "fr-FR"). name: Optional label for this entry (e.g. "homepage hero copy"). |
| get_languagesA | Get all languages configured for the Nativ workspace. Returns language names, codes, formality settings, and custom style directives for each language. |
| get_translation_memory_statsB | Get statistics about the translation memory. Shows total entries, enabled/disabled counts, and breakdown by source type. |
| get_style_guidesA | Get all style guides configured for the workspace. Returns the titles, content, and enabled status of each style guide. |
| get_brand_voiceA | Get the brand voice prompt — the core localization personality. This is the master prompt that shapes all translations. It captures the brand's tone, personality, terminology, and localization guidelines. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| localize_content | Localize content into target languages using Nativ. Args: content: The content to localize. target_languages: Comma-separated list of target languages (e.g. "French, German, Japanese"). Leave empty to use all configured languages. context: Optional context about the content (e.g. "marketing email subject line"). |
| review_translation | Review a translation for quality and consistency with TM and style guides. Args: source_text: The original text. translated_text: The translation to review. target_language: The target language name. |
| batch_localize_strings | Batch-localize i18n strings for a software project. Args: strings: The strings to localize — can be JSON, CSV, or one-per-line. target_languages: Comma-separated target languages. Leave empty for all configured. format_hint: Expected output format: "json", "csv", or "plain". |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| resource_languages | Configured languages with formality and style settings. |
| resource_style_guides | All style guides including enabled/disabled status. |
| resource_brand_prompt | The master brand voice prompt used for all translations. |
| resource_tm_stats | Translation memory statistics and source breakdown. |
TDQS
Scored across 8 tools
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.
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.
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.
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.