Checks the status of a verified translation job.

Creates a new trained translation model for a specific language pair. Use this tool when a user wants to improve translation quality for a specific language pair by creating a translation model that can be trained with their own data. This is a prerequisite for using `translate_files_with_verification` for that language pair.

Triggers a job export and returns a download link for the completed job.

Retrieves all available credit balances for the authenticated user. Instant credits refresh every month. You get a certain amount to use every month. Every month the credits refresh. Verified credits don't expire. Once they are purchased you will always have them. Use this tool to check the current balance for both instant and verified translation credits. Returns: dict[str, int]: A dictionary representing the credits with the following keys: - instant_translation(int): The number of instant translation credits - verified_translation(int): The number of verified translation credits

Returns a friendly hello world message.

Lists and filters jobs or models.

Create a verified translation job that will be assigned to professional LILT linguists for translation and review. IMPORTANT TWO-STEP WORKFLOW: STEP 1 - Get Quote (user_confirmed=False, default): - Call this tool WITHOUT user_confirmed or with user_confirmed=False - Returns a quote showing credits needed, current balance, and detailed breakdown - NO job is created, NO credits are deducted - Show the user the quote and ask for explicit confirmation STEP 2 - Create Job (user_confirmed=True): - Only after user explicitly confirms, call this tool again with user_confirmed=True - Creates the job and deducts credits - Returns the created job details Credit Handling: - If insufficient_credits=True: Inform user they don't have enough credits and suggest purchasing more at https://mcp.lilt.com/home - If credits are sufficient but low (10 or fewer): Warn user about low balance and suggest purchasing more credits - If credits are sufficient: Show quote details (credits needed, current balance, balance after) and ASK FOR EXPLICIT CONFIRMATION before proceeding to step 2 If the tool throws a ValueError related to no trained models existing for a language pair, then tell this to the user and ask if they would like to create a trained model.

Translates text using LILT's instant translate API. Use this tool when you need to translate text from one language to another using LILT's neural translation models. IMPORTANT: Check the 'insufficient_credits' field in the response. If True, inform the user they don't have enough credits and suggest purchasing more instant translation credits at https://mcp.lilt.com/home. If the user has sufficient credits but their balance is running low (5,000 credits or fewer), inform them about the remaining balance and suggest purchasing more credits to avoid interruptions. In the response object, if the "used_trained_model" is false, then tell the user that the translation was done with a LILT base model. If the user wants to improve the translation quality or match their brand voice they should create a trained model. Ask the user if they would like to create a trained model.

Upload a file to LILT for translation. This tool handles file uploads. For non-plain-text file formats like PDF, DOCX, RTF, etc., it assumes the client has extracted the text content. To indicate that only the text is being used, the tool appends a `.txt` extension to the original filename (e.g., `report.docx` becomes `report.docx.txt`). IMPORTANT: You must inform the user of this behavior. When a user uploads a file like a PDF or DOCX, confirm by saying: "I am uploading the extracted text from your file [original_filename] for translation."