How to Use the Localazy (AI Translation & Localization API) MCP in LangChain

Q: How do I connect the Localazy (AI Translation & Localization API) MCP Server to LangChain?

Install the langchain-mcp-adapters package first. Use MultiServerMCPClient to point to the Vinkius MCP endpoint, pull the tools, and pass them to your agent.

Q: Can LangChain agents handle batch updates on Localazy?

Yes, your agent can run setkeytags or setkeypriority in a single chain step. This allows for bulk categorization of up to 1,000 keys at once.

Q: How does LangChain trace Localazy (AI Translation & Localization API) tool execution?

Every tool call like listprojects or downloadfile goes through LangSmith. You see the exact payload sent to Localazy and the response latency in your dashboard.

Q: Can I download translated files directly in my chain?

Yes, use the downloadfile tool to fetch raw translation files in any target language. Your agent can then write these files directly to your repository.

Get your LangChain agents to manage translation keys, upload files, and sync glossaries with Localazy.

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Connect Localazy (AI Translation & Localization API) MCP to LangChain

Create your Vinkius account to connect Localazy (AI Translation & Localization API) to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Localazy (AI Translation & Localization API) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automated localization chains

This MCP server exposes the `import_content` tool to let your AI client push source files directly into your localization pipeline. Your LangChain agent can grab raw JSON or YAML strings from a previous step, run checks, and fire them straight into Localazy without you touching a CLI. You can chain this with `list_files` to keep track of what's already on the platform. The agent handles the file list, detects missing assets, and runs updates based on real-time code changes.

Smart glossary management with LangChain

The `create_glossary_term` tool lets your agent define critical terminology during translation cycles. Instead of letting automated translations guess what your product terms mean, the LangChain chain injects strict context definitions before the translation runs. If terms change, the agent uses `update_glossary_term` to keep the database fresh. This keeps your brand voice consistent across thirty languages without manual copy-pasting.

Visual context mapping

The `upload_screenshot` tool is what you use to give human translators visual context for specific keys. Your agent can run this tool via the MCP Server to pair newly created keys with UI screenshots from your build folder. Use `update_screenshot` to link those images to the exact keys modified in a pull request. It stops translators from guessing where text appears on the screen.

Setup guide

Set up Localazy (AI Translation & Localization API) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Localazy (AI Translation & Localization API) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "localazy-ai-translation-localization-api-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Localazy (AI Translation & Localization API) transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Localazy. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Localazy (AI Translation & Localization API) MCP in LangChain

Install the `langchain-mcp-adapters` package first. Use `MultiServerMCPClient` to point to the Vinkius MCP endpoint, pull the tools, and pass them to your agent.

Yes, your agent can run `set_key_tags` or `set_key_priority` in a single chain step. This allows for bulk categorization of up to 1,000 keys at once.

Every tool call like `list_projects` or `download_file` goes through LangSmith. You see the exact payload sent to Localazy and the response latency in your dashboard.

Yes, use the `download_file` tool to fetch raw translation files in any target language. Your agent can then write these files directly to your repository.

This MCP server runs in a secure V8 sandbox on Vinkius, meaning your translation keys and glossary terms are never exposed. Your API token is handled securely at the proxy level, preventing raw credentials from leaking into your application logs.

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