AI Translation▌
by datanoisetv
AI Translation offers an advanced AI translation and machine translation service, auto-translating JSON files with perfe
Translates JSON internationalization files using multiple translation providers (Google Gemini, OpenAI, Ollama/DeepSeek) with intelligent caching, deduplication across files, and format preservation to minimize API costs while maintaining exact JSON structure and consistent results across target languages.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Frontend developers localizing applications
- / Teams managing multi-language i18n files
- / Projects requiring cost-effective bulk translation
- / Maintaining consistent translations across multiple JSON files
capabilities
- / Translate JSON internationalization files to multiple languages
- / Process multiple files with automatic deduplication
- / Cache translations incrementally to avoid re-translating
- / Batch translations for optimal API performance
- / Preserve JSON structure and formatting exactly
- / Detect source language automatically
what it does
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.
about
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.
how to install
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.
license
NOASSERTION
AI Translation is released under the NOASSERTION license.
readme
translator-ai
Fast and efficient JSON i18n translator supporting multiple AI providers (Google Gemini, OpenAI & Ollama/DeepSeek) with intelligent caching, multi-file deduplication, and MCP integration.
Features
- Multiple AI Providers: Choose between Google Gemini, OpenAI (cloud) or Ollama/DeepSeek (local) for translations
- Multi-File Support: Process multiple files with automatic deduplication to save API calls
- Incremental Caching: Only translates new or modified strings, dramatically reducing API calls
- Batch Processing: Intelligently batches translations for optimal performance
- Path Preservation: Maintains exact JSON structure including nested objects and arrays
- Cross-Platform: Works on Windows, macOS, and Linux with automatic cache directory detection
- Developer Friendly: Built-in performance statistics and progress indicators
- Cost Effective: Minimizes API usage through smart caching and deduplication
- Language Detection: Automatically detect source language instead of assuming English
- Multiple Target Languages: Translate to multiple languages in a single command
- Translation Metadata: Optionally include translation details in output files for tracking
- Dry Run Mode: Preview what would be translated without making API calls
- Format Preservation: Maintains URLs, emails, dates, numbers, and template variables unchanged
Installation
Global Installation (Recommended)
npm install -g translator-ai
Local Installation
npm install translator-ai
Configuration
Option 1: Google Gemini API (Cloud)
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.
Option 2: OpenAI API (Cloud)
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.
Option 3: Ollama with DeepSeek-R1 (Local)
For completely local translation without API costs:
- Install Ollama
- Pull the DeepSeek-R1 model:
ollama pull deepseek-r1:latest - Use the
--provider ollamaflag:translator-ai source.json -l es -o spanish.json --provider ollama
Usage
Basic Usage
# 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"
Command Line Options
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
Examples
Translate a single file
translator-ai en.json -l es -o es.json
Translate multiple files with pattern
# 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"
Translate with deduplication savings
# Shows statistics including how many API calls were saved
translator-ai src/i18n/*.json -l ja -o "{dir}/{name}.{lang}.json" --stats
Output to stdout (useful for piping)
translator-ai en.json -l de --stdout > de.json
Parse output with jq
translator-ai en.json -l de --stdout | jq
Disable caching for fresh translation
translator-ai en.json -l ja -o ja.json --no-cache
Use custom cache location
translator-ai en.json -l ko -o ko.json --cache-file /path/to/cache.json
Use Ollama for local translation
# 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
Advanced Features
# 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
Available Gemini Models
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 model
Example 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
Available OpenAI Models
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 translations
Example 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
Translation Metadata
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.
Key Sorting
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:
- Case-insensitiv
FAQ
- What is the AI Translation MCP server?
- AI Translation is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for AI Translation?
- This profile displays 65 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★65 reviews- ★★★★★Diya Menon· Dec 28, 2024
AI Translation is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Diya Tandon· Dec 20, 2024
According to our notes, AI Translation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Diego Huang· Dec 16, 2024
We evaluated AI Translation against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Alexander Zhang· Dec 8, 2024
We wired AI Translation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Alexander Smith· Nov 27, 2024
According to our notes, AI Translation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Diya Verma· Nov 19, 2024
AI Translation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★James Jackson· Nov 11, 2024
We wired AI Translation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Alexander Anderson· Oct 18, 2024
AI Translation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Amelia Martinez· Oct 10, 2024
According to our notes, AI Translation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Nikhil Patel· Oct 2, 2024
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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