Crowdin

Without context translations can quickly become a real headache. What’s the most challenging single word you have ever translated? Every translator knows the sinking feeling of staring at a text string that could mean ten different things. Is “Charge.” a command in a battlefield game? Is it a prompt on a telephone screen? Is it a noun for an invoice? Or is it the fee on your credit card statement? What’s your go-to method for chasing down missing context without access to the client? When all you have is the word, you’re forced to guess. Sometimes you get it right. Sometimes you don’t. And every wrong guess has a cost, rework, confusion, and in some cases, an entire feature that doesn’t make sense in the target language. Up until now, the promise of AI in localization has been tied to RAG (Retrieval-Augmented Generation). RAG can reference supporting documentation to give you more background but by default that content is static. A frozen snapshot of a moment in time. It can tell you where “Charge” appears in a document you uploaded. It can’t, however, show you the button it’s attached to dynamically. It can’t fetch the developer’s comment explaining that it’s actually a battery level indicator. MCP (Model Context Protocol) takes “in the moment” context research to a new level. MCP has the ability to connect directly to live systems and resonate deeper through instructions. It can pull the actual screenshot from a design tool, the metadata from a TMS, the exact component name from the codebase. It can do the required research on the fly and give you the story behind the string, in real time, while you’re translating. With MCP, “Charge” stops being a puzzle and becomes a clear, unambiguous instruction in context. There’s no guessing. There’s no wasted time chasing down answers. There’s just translation…accurate, confident, and in context for a very specific use case without having to rely on a client to tip the veil for you. Sounds too good to be true? Localization AI Trainer Dorota Pawlak and I demo how it really works in the second episode of our Crowdin Power Up series in which we explore how MCP works on the plate and why it could finally solve one of the most frustrating problems for linguists: the lack of reliable, immediate context. If you could wave a magic wand and get instant context for every string, how would it change your workflow? If you’ve ever wished your tools simply knew what you were looking at, this episode of Crowdin Power Up is for you. Check it out with link in comments. In the meantime, I’d love to hear your stories. What’s the most ambiguous string you’ve ever had to translate without context? Bonus Opportunity: if you are a translator passionate about the latest in language technology and would like to partake in our free Crowdin Power Up Language Wizard initiative there are a couple of spots left to help us test. Drop us a comment below and we will send you a quick application.