DeepL

- Translate text between numerous languages
- Rephrase text using DeepL's capabilities
- Access to all DeepL API languages and features
- Automatic language detection
- Formality control for supported languages
- Batch translation and document translation
- Usage and quota reporting
- Translation history and usage analysis
- Support for multiple MCP transports: stdio, SSE, and Streamable HTTP

git clone https://github.com/AlwaysSany/deepl-fastmcp-python-server.git cd deepl-fastmcp-python-server

Create a.envfile or exportDEEPL_AUTH_KEYin your shell.You can do this by running the following command and then update the.envfile with your DeepL API key:

To run withStreamable HTTPtransport (recommended for web deployments):

uv run python main.py --transport streamable-http --host 127.0.0.1 --port 8000

uv run python main.py --transport sse --host 127.0.0.1 --port 8000

It will show some messages in the terminal like this:

Spawned stdio transport Connected MCP client to backing server transport

🔍 MCP Inspector is up and running athttp://127.0.0.1:6274

docker run -e DEEPL_AUTH_KEY=your_deepl_api_key -p 8000:8000 deepl-fastmcp-server

You'll need a DeepL API key to use this server. You can get one by signing up atDeepL API. With a DeepL API Free account you can translate up to 500,000 characters/month for free.

{ "mcpServers": { "deepl-fastmcp": { "command": "uv", "args": [ "--directory", "/path/to/yourdeepl-fastmcp-python-server/.venv", "run", "--with", "mcp", "python", "/path/to/your/deepl-fastmcp-python-server/main.py", "--transport", "streamable-http", "--host", "127.0.0.1", "--port", "8000" ] } } }

Note: To use Streamable HTTP or SSE transports with Cursor IDE, change the"--transport", "stdio"line to"--transport", "streamable-http", "--host", "127.0.0.1", "--port", "8000"or"--transport", "sse", "--host", "127.0.0.1", "--port", "8000"respectively, and adjust the host and port as needed.

"mcpServers": { "deepl-fastmcp": { "type": "sse", "url": "http://127.0.0.1:8000/sse" } }

and then run mcp server from terminaluv run main.py --transport sse --host 127.0.0.1 --port 8000

This MCP server integrates with Claude Desktop to provide translation capabilities directly in your conversations with Claude.
-

Install Claude Desktop if you haven't already

Create or edit the Claude Desktop configuration file:

- On macOS:~/Library/Application Support/Claude/claude_desktop_config.json
- On Windows:%AppData%\Claude\claude_desktop_config.json
- On Linux:~/.config/Claude/claude_desktop_config.json

{ "mcpServers": { "deepl-fastmcp": { "command": "uv", "args": [ "--directory", "/path/to/yourdeepl-fastmcp-python-server/.venv", "run", "--with", "mcp", "python", "/path/to/your/deepl-fastmcp-python-server/main.py", "--transport", "streamable-http", "--host", "127.0.0.1", "--port", "8000" ] } } }

Note: To use Streamable HTTP or SSE transports with Claude Desktop, change the"--transport", "stdio"line to"--transport", "streamable-http", "--host", "127.0.0.1", "--port", "8000"or"--transport", "sse", "--host", "127.0.0.1", "--port", "8000"respectively, and adjust the host and port as needed.

This server provides the following tools:

- translate_text: Translate text to a target language
- rephrase_text: Rephrase text in the same or different language
- batch_translate: Translate multiple texts in a single request
- translate_document: Translate a document file using DeepL API
- detect_language: Detect the language of given text
- get_translation_history: Get recent translation operation history
- analyze_usage_patterns: Analyze translation usage patterns from history

The following resources are available for read-only data access (can be loaded into LLM context):

- usage://deepl: DeepL API usage info.
- deepl://languages/source: Supported source languages.
- deepl://languages/target: Supported target languages.
- deepl://glossaries: Supported glossary language pairs.
- history://translations: Recent translation operation history (same asget_translation_historytool)
- usage://patterns: Usage pattern analysis (same asanalyze_usage_patternstool)

The following prompt is available for LLMs:

-

summarize: Returns a message instructing the LLM to summarize a given text.

@mcp.prompt("summarize") def summarize_prompt(text: str) -> str: return f"Please summarize the following text:\n\n{text}"

Translate text between languages using the DeepL API.

- Parameters:

- text: The text to translate
- target_language: Target language code (e.g., 'EN', 'DE', 'FR', 'ES', 'IT', 'JA', 'ZH')
- source_language(optional): Source language code
- formality(optional): Controls formality level ('less', 'more', 'default', 'prefer_less', 'prefer_more')
- preserve_formatting(optional): Whether to preserve formatting
- split_sentences(optional): How to split sentences
- tag_handling(optional): How to handle tags

Rephrase text in the same or different language using the DeepL API.

- Parameters:

- text: The text to rephrase
- target_language: Language code for rephrasing
- formality(optional): Desired formality level
- context(optional): Additional context for better rephrasing

Translate multiple texts in a single request.

- Parameters:

- texts: List of texts to translate
- target_language: Target language code
- source_language(optional): Source language code
- formality(optional): Formality level
- preserve_formatting(optional): Whether to preserve formatting

Translate a document file using DeepL API.

- Parameters:

- file_path: Path to the document file
- target_language: Target language code
- output_path(optional): Output path for translated document
- formality(optional): Formality level
- preserve_formatting(optional): Whether to preserve document formatting

Detect the language of given text using DeepL.

- Parameters:

- text: Text to analyze for language detection

- No parameters required. See tool output for details.

- No parameters required. See tool output for details.

The DeepL API supports a wide variety of languages for translation. You can use theget_source_languagesandget_target_languagestools, or thedeepl://languages/sourceanddeepl://languages/targetresources, to see all currently supported languages.

Some examples of supported languages include:

- English (en, en-US, en-GB)
- German (de)
- Spanish (es)
- French (fr)
- Italian (it)
- Japanese (ja)
- Chinese (zh)
- Portuguese (pt-BR, pt-PT)
- Russian (ru)
- And many more

For debugging information, visit theMCP debugging documentation.

uv pip compile pyproject.toml > requirements.txt

echo "python-3.13.3" > runtime.txt

python main.py --transport sse --host 0.0.0.0 --port 8000

Live Endpoint:
https://deepl-fastmcp-python-server.onrender.com/sse

- Author:Sany Ahmed
- Email:sany2k8@gmail.com

- DeepL API Documentation
- Model Context Protocol Documentation

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