MCP server
by datanoisetv
AI Translation offers an advanced AI translation and machine translation service, auto-translating JSON files with perfe
Translates JSON i18n files using AI providers (Google Gemini, OpenAI, Ollama/DeepSeek) while preserving exact JSON structure and minimizing API costs through intelligent caching and deduplication.
AI Translation is a community-built MCP server published by datanoisetv that provides AI assistants with tools and capabilities via the Model Context Protocol. AI Translation offers an advanced AI translation and machine translation service, auto-translating JSON files with perfe It is categorized under ai ml, developer tools.
You can install AI Translation in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
NOASSERTION
AI Translation is released under the NOASSERTION license.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
AI Translation is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
According to our notes, AI Translation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We evaluated AI Translation against two servers with overlapping tools; this profile had the clearer scope statement.
We wired AI Translation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, AI Translation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
AI Translation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We wired AI Translation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
AI Translation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, AI Translation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
AI Translation is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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Fast and efficient JSON i18n translator supporting multiple AI providers (Google Gemini, OpenAI & Ollama/DeepSeek) with intelligent caching, multi-file deduplication, and MCP integration.
npm install -g translator-ai
npm install translator-ai
Create a .env file in your project root or set the environment variable:
GEMINI_API_KEY=your_gemini_api_key_here
Get your API key from Google AI Studio.
Create a .env file in your project root or set the environment variable:
OPENAI_API_KEY=your_openai_api_key_here
Get your API key from OpenAI Platform.
For completely local translation without API costs:
ollama pull deepseek-r1:latest
--provider ollama flag:
translator-ai source.json -l es -o spanish.json --provider ollama
# Translate a single file
translator-ai source.json -l es -o spanish.json
# Translate multiple files with deduplication
translator-ai src/locales/en/*.json -l es -o "{dir}/{name}.{lang}.json"
# Use glob patterns
translator-ai "src/**/*.en.json" -l fr -o "{dir}/{name}.fr.json"
translator-ai <inputFiles...> [options]
Arguments:
inputFiles Path(s) to source JSON file(s) or glob patterns
Options:
-l, --lang <langCodes> Target language code(s), comma-separated for multiple
-o, --output <pattern> Output file path or pattern
--stdout Output to stdout instead of file
--stats Show detailed performance statistics
--no-cache Disable incremental translation cache
--cache-file <path> Custom cache file path
--provider <type> Translation provider: gemini, openai, or ollama (default: gemini)
--ollama-url <url> Ollama API URL (default: http://localhost:11434)
--ollama-model <model> Ollama model name (default: deepseek-r1:latest)
--gemini-model <model> Gemini model name (default: gemini-2.0-flash-lite)
--openai-model <model> OpenAI model name (default: gpt-4o-mini)
--list-providers List available translation providers
--verbose Enable verbose output for debugging
--detect-source Auto-detect source language instead of assuming English
--dry-run Preview what would be translated without making API calls
--preserve-formats Preserve URLs, emails, numbers, dates, and other formats
--metadata Add translation metadata to output files (may break some i18n parsers)
--sort-keys Sort output JSON keys alphabetically
--check-keys Verify all source keys exist in output (exit with error if keys are missing)
-h, --help Display help
-V, --version Display version
Output Pattern Variables (for multiple files):
{dir} - Original directory path
{name} - Original filename without extension
{lang} - Target language code
translator-ai en.json -l es -o es.json
# All JSON files in a directory
translator-ai locales/en/*.json -l es -o "locales/es/{name}.json"
# Recursive glob pattern
translator-ai "src/**/en.json" -l fr -o "{dir}/fr.json"
# Multiple specific files
translator-ai file1.json file2.json file3.json -l de -o "{name}.de.json"
# Shows statistics including how many API calls were saved
translator-ai src/i18n/*.json -l ja -o "{dir}/{name}.{lang}.json" --stats
translator-ai en.json -l de --stdout > de.json
translator-ai en.json -l de --stdout | jq
translator-ai en.json -l ja -o ja.json --no-cache
translator-ai en.json -l ko -o ko.json --cache-file /path/to/cache.json
# Basic usage with Ollama
translator-ai en.json -l es -o es.json --provider ollama
# Use a different Ollama model
translator-ai en.json -l fr -o fr.json --provider ollama --ollama-model llama2:latest
# Connect to remote Ollama instance
translator-ai en.json -l de -o de.json --provider ollama --ollama-url http://192.168.1.100:11434
# Check available providers
translator-ai --list-providers
# Detect source language automatically
translator-ai content.json -l es -o spanish.json --detect-source
# Translate to multiple languages at once
translator-ai en.json -l es,fr,de,ja -o translations/{lang}.json
# Dry run - see what would be translated without making API calls
translator-ai en.json -l es -o es.json --dry-run
# Preserve formats (URLs, emails, dates, numbers, template variables)
translator-ai app.json -l fr -o app-fr.json --preserve-formats
# Include translation metadata (disabled by default to ensure compatibility)
translator-ai en.json -l fr -o fr.json --metadata
# Sort keys alphabetically for consistent output
translator-ai en.json -l fr -o fr.json --sort-keys
# Verify all keys are present in the translation
translator-ai en.json -l fr -o fr.json --check-keys
# Use a different Gemini model
translator-ai en.json -l es -o es.json --gemini-model gemini-2.5-flash
# Combine features
translator-ai src/**/*.json -l es,fr,de -o "{dir}/{name}.{lang}.json" \
--detect-source --preserve-formats --stats --check-keys
The --gemini-model option allows you to choose from various Gemini models. Popular options include:
gemini-2.0-flash-lite (default) - Fast and efficient for most translationsgemini-2.5-flash - Enhanced performance with newer capabilitiesgemini-pro - More sophisticated understanding for complex translationsgemini-1.5-pro - Previous generation pro modelgemini-1.5-flash - Previous generation fast modelExample usage:
# Use the latest flash model
translator-ai en.json -l es -o es.json --gemini-model gemini-2.5-flash
# Use the default lightweight model
translator-ai en.json -l fr -o fr.json --gemini-model gemini-2.0-flash-lite
The --openai-model option allows you to choose from various OpenAI models. Popular options include:
gpt-4o-mini (default) - Cost-effective and fast for most translationsgpt-4o - Most capable model with advanced understandinggpt-4-turbo - Previous generation flagship modelgpt-3.5-turbo - Fast and efficient for simpler translationsExample usage:
# Use OpenAI with the default model
translator-ai en.json -l es -o es.json --provider openai
# Use GPT-4o for complex translations
translator-ai en.json -l ja -o ja.json --provider openai --openai-model gpt-4o
# Use GPT-3.5-turbo for faster, simpler translations
translator-ai en.json -l fr -o fr.json --provider openai --openai-model gpt-3.5-turbo
When enabled with the --metadata flag, translator-ai adds metadata to help track translations:
{
"_translator_metadata": {
"tool": "translator-ai v1.1.0",
"repository": "https://github.com/DatanoiseTV/translator-ai",
"provider": "Google Gemini",
"source_language": "English",
"target_language": "fr",
"timestamp": "2025-06-20T12:34:56.789Z",
"total_strings": 42,
"source_file": "en.json"
},
"greeting": "Bonjour",
"farewell": "Au revoir"
}
Metadata is disabled by default to ensure compatibility with i18n parsers. Use --metadata to enable it.
Use the --sort-keys flag to sort all JSON keys alphabetically in the output:
translator-ai en.json -l es -o es.json --sort-keys
This ensures consistent ordering across translations and makes diffs cleaner. Keys are sorted:
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.