Textra Japanese to English Translator

Textra Japanese to English Translator MCP Server

Overview

This project provides a Model Context Protocol (MCP) server that translates Japanese text into English using the textra translation API service.

It is particularly useful for interacting with LLMs that have limited Japanese language understanding. By routing Japanese instructions through this MCP server, the input can be translated into English before being passed to the LLM.

This server is built using the fastmcp framework.

Setup

1. Clone the repository:
   git clone <repository_url> cd <repository_directory>
2. Create and activate a virtual environment:
   uv venv source .venv/bin/activate
   (On Windows, use .venv\Scripts\activate)
3. Install dependencies: Install the necessary libraries for running the project and for development/testing.
   # Install runtime dependencies only uv pip install . # Install runtime and development/testing dependencies uv pip install '.[dev]'
   (Dependencies are installed based on pyproject.toml.)
4. Set Environment Variables: This server requires the following environment variables to be set with your Textra API credentials:
   • TEXTRA_API_KEY: Your Textra API Key.
   • TEXTRA_API_SECRET: Your Textra API Secret.
   • TEXTRA_USER_NAME: Your Textra Login ID.

   It is strongly recommended to set these variables in your shell's configuration file (e.g., ~/.bashrc, ~/.zshrc, ~/.config/fish/config.fish) rather than using a .env file. This ensures the variables are available when the MCP server is run by client applications.

   Example for .zshrc or .bashrc:

   export TEXTRA_API_KEY="your_api_key" export TEXTRA_API_SECRET="your_api_secret" export TEXTRA_USER_NAME="your_username"

   Remember to source the file (e.g., source ~/.zshrc) or restart your shell after adding these lines.

   Optional Variables:

   • TEXTRA_JA_EN_API_URL: Overrides the default translation API endpoint.
   • TEXTRA_TOKEN_URL: Overrides the default OAuth token endpoint.

   (See .env.example for variable names and default values. While using a .env file is possible for local development, especially with fastmcp dev, setting system-wide environment variables is more robust for MCP server deployment.)

Running Tests

Ensure your virtual environment is activated.

or

Usage

Ensure your virtual environment is activated and the required environment variables (TEXTRA_API_KEY, TEXTRA_API_SECRET, TEXTRA_USER_NAME) are correctly set in your shell environment.

Running the Server Locally

There are two main ways to run the server locally:

1. Using fastmcp dev (Recommended for development): This command provides features like auto-reloading on code changes.
   fastmcp dev server.py
2. Using uvx: This command runs the server script directly within a temporary environment managed by uv, installing dependencies as needed. It's useful for quick execution without installation.
   uvx python server.py

Both commands will start the server, listening for MCP connections via stdio by default. You can then connect to it using an MCP client like the MCP Inspector.

3. Using uvx directly from GitHub (Requires uv installed): You can run the server directly from the GitHub repository without cloning it first. This is useful for integrating with MCP clients that support custom commands. uvx will handle fetching the code and installing dependencies in a temporary environment.
   uvx python server.py --git https://github.com/hokupod/textra-ja-to-en-mcp.git
   Note: This method might take longer to start initially compared to running from a local clone.

Installing the Server for MCP Clients (e.g., Claude Desktop)

Use the fastmcp install command to make the server available to MCP client applications on your system.

After installation, MCP clients like Claude Desktop should be able to discover and use the "Japanese to English Translator" tool.

Alternatively, if your MCP client supports defining servers via commands (like Claude Desktop's mcp_servers.json), you can configure it to run the server directly from GitHub using uvx:

(Ensure uv is installed and accessible in the client's environment.)

When interacting with an LLM through such a client, if you provide input in Japanese, the LLM (if configured to use this tool appropriately based on its description) should automatically invoke this server to translate the text to English before processing the request further. The translated English text will then be treated as the user's original request.