Lara Translate MCP Server

What does “Lara Translate” MCP Server do?

Lara Translate MCP Server is a Model Context Protocol (MCP) server that connects AI assistants and applications to the Lara Translate API, enabling professional-grade translation capabilities. By acting as a bridge between AI models and the translation service, it allows seamless integration for tasks such as language detection, context-aware translations, and leveraging translation memories. The server enables AI applications to securely and flexibly perform translations, discover available tools and resources, and handle translation requests with structured parameters. This approach enhances development workflows, allowing applications to offer high-quality translations without directly managing the underlying API, while maintaining the security of API credentials and supporting advanced features for non-English languages.

List of Prompts

No explicit prompt templates are listed in the available documentation or repository files.

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List of Resources

No explicit MCP resources are described in the available documentation or repository files.

List of Tools

• Translation Tool: Provides access to Lara Translate’s core translation capabilities, allowing structured requests for text translation, language detection, and context-aware translation processing.

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Use Cases of this MCP Server

• Multilingual Content Generation: Automatically translate content into multiple languages for global audiences without manual intervention.
• Context-Aware Translations: Enhance translation accuracy by leveraging context and translation memories, benefiting applications that need domain-specific language.
• Seamless Workflow Integration: Integrate professional translation into existing AI-driven workflows, such as chatbots or document processing systems, without direct API calls.
• Language Detection for AI Agents: Equip AI agents with the ability to detect the language of input text, improving user experience and routing.
• Secure Credential Management: Centralize translation logic and security by keeping API credentials within the MCP server, reducing exposure in client applications.

How to set it up

Windsurf

1. Ensure you have Node.js installed.
2. Locate your windsurf.json or equivalent configuration file.
3. Add the Lara Translate MCP server to the mcpServers section:

   {
     "mcpServers": {
       "lara-mcp": {
         "command": "npx",
         "args": ["@translated/lara-mcp@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the server is running by checking the Windsurf logs.

Securing API Keys:

{
  "lara-mcp": {
    "env": {
      "LARA_API_KEY": "your-api-key"
    },
    "inputs": {
      "apiKey": "${LARA_API_KEY}"
    }
  }
}

Claude

1. Install Node.js if not already present.
2. Open your Claude configuration file.
3. Add Lara Translate MCP to the mcpServers configuration:

   {
     "mcpServers": {
       "lara-mcp": {
         "command": "npx",
         "args": ["@translated/lara-mcp@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Check Claude’s status dashboard to confirm integration.

Securing API Keys:

{
  "lara-mcp": {
    "env": {
      "LARA_API_KEY": "your-api-key"
    },
    "inputs": {
      "apiKey": "${LARA_API_KEY}"
    }
  }
}

Cursor

1. Ensure Node.js is installed on your system.
2. Edit the Cursor configuration file.
3. Add the Lara Translate MCP server as follows:

   {
     "mcpServers": {
       "lara-mcp": {
         "command": "npx",
         "args": ["@translated/lara-mcp@latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Confirm setup by running a test translation.

Securing API Keys:

{
  "lara-mcp": {
    "env": {
      "LARA_API_KEY": "your-api-key"
    },
    "inputs": {
      "apiKey": "${LARA_API_KEY}"
    }
  }
}

Cline

1. Make sure Node.js is available.
2. Open the Cline configuration file.
3. Add Lara Translate MCP in the mcpServers section:

   {
     "mcpServers": {
       "lara-mcp": {
         "command": "npx",
         "args": ["@translated/lara-mcp@latest"]
       }
     }
   }
   

4. Save your changes and restart Cline.
5. Verify functionality through a sample translation request.

Securing API Keys:

{
  "lara-mcp": {
    "env": {
      "LARA_API_KEY": "your-api-key"
    },
    "inputs": {
      "apiKey": "${LARA_API_KEY}"
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "lara-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “lara-mcp” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the available documentation, Lara Translate MCP provides a robust translation tool and clear setup instructions, but lacks explicit prompt templates, MCP resource listings, and sampling/root support documentation. Overall, it is a focused, practical MCP server for translation tasks.

MCP Score