Lara Translate MCP

How to Install and Use Lara Translate MCP

Lara Translate MCP is a powerful tool that allows AI applications to connect easily with Lara Translate’s professional translation services. It uses a standard called Model Context Protocol (MCP), which helps applications send text to be translated and receive translations back.

Here’s a simple guide on how to install and use Lara Translate MCP with practical steps and example code.

Setting Up the HTTP Server

If your MCP client does not support direct URL configuration (like Claude or OpenAI), you will set up Lara Translate MCP using a Node.js command.

First, you need to add this configuration to your client’s MCP settings:

{
  "mcpServers": {
    "lara": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.laratranslate.com/v1",
        "--header",
        "x-lara-access-key-id: ${X_LARA_ACCESS_KEY_ID}",
        "--header",
        "x-lara-access-key-secret: ${X_LARA_ACCESS_KEY_SECRET}"
      ],
      "env": {
        "X_LARA_ACCESS_KEY_ID": "<YOUR_ACCESS_KEY_ID>",
        "X_LARA_ACCESS_KEY_SECRET": "<YOUR_ACCESS_KEY_SECRET>"
      }
    }
  }
}

Replace <YOUR_ACCESS_KEY_ID> and <YOUR_ACCESS_KEY_SECRET> with your actual Lara Translate API credentials. After that, just restart your MCP client to apply the changes.

For clients that do support URL configuration (like Cursor or Continue), you can use a simpler setup. Add this to your MCP configuration:

{
  "mcpServers": {
    "lara": {
      "url": "https://mcp.laratranslate.com/v1",
      "headers": {
        "x-lara-access-key-id": "<YOUR_ACCESS_KEY_ID>",
        "x-lara-access-key-secret": "<YOUR_ACCESS_KEY_SECRET>"
      }
    }
  }
}

Again, replace the placeholders with your own API credentials and restart your client.

Installing with the STDIO Server

If you want to run Lara Translate MCP locally or through Docker, it supports STDIO mode.

Using NPX (Node.js)

You can add this block to your MCP configuration:

{
  "mcpServers": {
    "lara-translate": {
      "command": "npx",
      "args": ["-y", "@translated/lara-mcp@latest"],
      "env": {
        "LARA_ACCESS_KEY_ID": "<YOUR_ACCESS_KEY_ID>",
        "LARA_ACCESS_KEY_SECRET": "<YOUR_ACCESS_KEY_SECRET>"
      }
    }
  }
}

Replace the keys with your credentials, then restart your AI client.

Using Docker

If you prefer Docker, use this configuration:

{
  "mcpServers": {
    "lara-translate": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "LARA_ACCESS_KEY_ID",
        "-e",
        "LARA_ACCESS_KEY_SECRET",
        "translatednet/lara-mcp:latest"
      ],
      "env": {
        "LARA_ACCESS_KEY_ID": "<YOUR_ACCESS_KEY_ID>",
        "LARA_ACCESS_KEY_SECRET": "<YOUR_ACCESS_KEY_SECRET>"
      }
    }
  }
}

Make sure Docker is installed on your system. Replace <YOUR_ACCESS_KEY_ID> and <YOUR_ACCESS_KEY_SECRET> with your real keys.

Building from Source

If you want full control and prefer building the server yourself, follow these instructions.

First, clone the repository:

git clone https://github.com/translated/lara-mcp.git
cd lara-mcp

Then install dependencies and build the project:

pnpm install
pnpm run build

Finally, add this configuration to your client, adjusting the path to your build folder:

{
  "mcpServers": {
    "lara-translate": {
      "command": "node",
      "args": ["<FULL_PATH_TO_PROJECT_FOLDER>/dist/index.js"],
      "env": {
        "LARA_ACCESS_KEY_ID": "<YOUR_ACCESS_KEY_ID>",
        "LARA_ACCESS_KEY_SECRET": "<YOUR_ACCESS_KEY_SECRET>"
      }
    }
  }
}

Replace the placeholders with your actual project path and API credentials.

Verifying the Installation

Once configured and restarted, check if Lara Translate MCP appears in your MCP client’s list. To test it, try translating a simple phrase using your client command like:

Translate with Lara "Hello world" to Spanish

If everything works, you should see the translation coming back from Lara Translate MCP.

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By following these steps, you can quickly set up Lara Translate MCP to add professional translation capabilities to your AI applications without coding the API directly. Just use your API keys, add the configuration, and start translating!