MCP server
by translated
Lara Translate offers language translation with automatic detection, from Latin translation to translate English to Geor
Connects to the Lara Translate API to provide professional-grade text translations with automatic language detection and context preservation.
Lara Translate is an official MCP server published by translated that provides AI assistants with tools and capabilities via the Model Context Protocol. Lara Translate offers language translation with automatic detection, from Latin translation to translate English to Geor It is categorized under ai ml.
You can install Lara Translate 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.
MIT
Lara Translate is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
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
Lara Translate is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Lara Translate is the kind of server we cite when onboarding engineers to host + tool permissions.
Strong directory entry: Lara Translate surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Lara Translate for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Lara Translate reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Lara Translate has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Lara Translate is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Strong directory entry: Lara Translate surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: Lara Translate is the kind of server we cite when onboarding engineers to host + tool permissions.
Lara Translate reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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A Model Context Protocol (MCP) Server for Lara Translate API, enabling powerful translation capabilities with support for language detection, context-aware translations and translation memories.
Model Context Protocol (MCP) is an open standardized communication protocol that enables AI applications to connect with external tools, data sources, and services. Think of MCP like a USB-C port for AI applications - just as USB-C provides a standardized way to connect devices to various peripherals, MCP provides a standardized way to connect AI models to different data sources and tools.
Lara Translate MCP Server enables AI applications to access Lara Translate's powerful translation capabilities through this standardized protocol.
</details> <details> <summary><strong>How Lara Translate MCP Works</strong></summary>More info about Model Context Protocol on: https://modelcontextprotocol.io/
Lara Translate MCP Server implements the Model Context Protocol to provide seamless translation capabilities to AI applications. The integration follows this flow:
This integration architecture allows AI applications to access professional-grade translations without implementing the API directly, while maintaining the security of your API credentials and offering flexibility to adjust translation parameters through natural language instructions.
</details> <details> <summary><strong>Why to use Lara inside an LLM</strong></summary>Integrating Lara with LLMs creates a powerful synergy that significantly enhances translation quality for non-English languages.
While large language models possess broad linguistic capabilities, they often lack the specialized expertise and up-to-date terminology required for accurate translations in specific domains and languages.
Lara overcomes this limitation by leveraging Translation Language Models (T-LMs) trained on billions of professionally translated segments. These models provide domain-specific machine translation that captures cultural nuances and industry terminology that generic LLMs may miss. The result: translations that are contextually accurate and sound natural to native speakers.
Lara has a strong focus on non-English languages, addressing the performance gap found in models such as GPT-4. The dominance of English in datasets such as Common Crawl and Wikipedia results in lower quality output in other languages. Lara helps close this gap by providing higher quality understanding, generation, and restructuring in a multilingual context.
By offloading complex translation tasks to specialized T-LMs, Lara reduces computational overhead and minimizes latency—a common issue for LLMs handling non-English input. Its architecture processes translations in parallel with the LLM, enabling for real-time, high-quality output without compromising speed or efficiency.
Lara also lowers the cost of using models like GPT-4 in non-English workflows. Since tokenization (and pricing) is optimized for English, using Lara allows translation to take place before hitting the LLM, meaning that only the translated English content is processed. This improves cost efficiency and supports competitive scalability for global enterprises.
</details>Inputs:
text (array): An array of text blocks to translate, each with:
text (string): The text contenttranslatable (boolean): Whether this block should be translatedsource (optional string): Source language code (e.g., 'en-EN')target (string): Target language code (e.g., 'it-IT')context (optional string): Additional context to improve translation qualityinstructions (optional string[]): Instructions to adjust translation behaviorsource_hint (optional string): Guidance for language detectionglossaries (optional string[]): Array of glossary IDs to enforce terminology (e.g., ['gls_xyz123'])no_trace (optional boolean): Privacy flag - if true, request won't be traced/loggedpriority (optional string): Translation priority - 'normal' or 'background'timeout_in_millis (optional number): Custom timeout in millisecondsReturns: Translated text blocks maintaining the original structure
</details>Inputs: None
Returns: Array of glossaries with their details (id, name, createdAt, updatedAt, ownerId)
</details> <details> <summary><strong>get_glossary</strong> - Get a specific glossary by ID</summary>Inputs:
id (string): The glossary ID (e.g., 'gls_xyz123')Returns: Glossary object or null if not found
</details>Returns: Array of memories and their details
</details> <details> <summary><strong>create_memory</strong> - Create a new translation memory</summary>Inputs:
name (string): Name of the new memoryexternal_id (optional string): ID of the memory to import from MyMemory (e.g., 'ext_my_[MyMemory ID]')Returns: Created memory data
</details> <details> <summary><strong>update_memory</strong> - Update translation memory name</summary>Inputs:
id (string): ID of the memory to updatename (string): The new name for the memoryReturns: Updated memory data
</details> <details> <summary><strong>delete_memory</strong> - Delete a translation memory</summary>Inputs:
id (string): ID of the memory to deleteReturns: Deleted memory data
</details> <details> <summary><strong>add_translation</strong> - Add a translation unit to memory</summary>Inputs:
id (string | string[]): ID or IDs of memories where to add the translation unitsource (string): Source language codetarget (string): Target language codesentence (string): The source sentencetranslation (string): The translated sentencetuid (optional string): Translation Unit unique identifiersentence_before (optional string): Context sentence beforesentence_after (optional string): Context sentence afterReturns: Added translation details
</details> <details> <summary><strong>delete_translation</strong> - Delete a translation unit from memory</summary>Inputs:
id (string): ID of the memorysource (string): Source language codetarget (string): Target language codesentence (string): The source sentencetranslation (string): The translated sentencetuid (optional string): Translation Unit unique identifiersentence_before (optional string): Context sentence beforesentence_after (optional string): Context sentence afterReturns: Removed translation details
</details> <details> <summary><strong>import_tmx</strong> - Import a TMX file into a memory</summary>Inputs:
id (string): ID of the memory to updatetmx_content (string): The content of the tmx file to uploadgzip (boolean): Indicates if the file is compressed (.gz)Returns: Import details
</details> <details> <summary><strong>check_import_status</strong> - Checks the status of a TMX file import</summary>Inputs:
id (string): The ID of the import jobReturns: Import details
</details>Lara supports both the STDIO and streamable HTTP protocols. For a hassle-free setup, we recommend using the HTTP protocol. If you prefer to use STDIO, it must be installed locally on your machine.
You'll find setup instructions for both protocols in the sections below.
Important: When running your own HTTP server instance (not using the remote https://mcp.laratranslate.com/v1), all connected clients share the same Lara API credentials configured via LARA_ACCESS_KEY_ID and LARA_ACCESS_KEY_SECRET environment
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.