How to Use the DeepL MCP in Google ADK

Q: How do I add DeepL to my Google ADK setup?

Install google-adk via pip. Initialize McpToolset with your Vinkius MCP Server endpoint and pass it into the LlmAgent under the tools parameter.

Q: Can Google ADK force DeepL to use our corporate glossary?

The agent reads your custom terms using getglossarydictionary. It applies those exact mappings during the translation process.

Q: What languages are available?

Call getsourcelanguages to see what the API accepts. The target list includes specific regional variations.

Q: Will Google ADK agents monitor my DeepL usage limits?

They absolutely do. The agent checks getapiusage to see your current character constraints before running heavy translation batches.

Connect the DeepL MCP Server to your Google ADK agents for massive-scale multilingual processing across BigQuery.

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Setup DeepL with Google ADK

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Multilingual BigQuery Pipelines

Huge tables of foreign text fit perfectly into Gemini's massive context window. Passing that data through `translate_text_standard` translates everything directly within your Google Cloud environment. Regional dialects require precision. Your agent can query `get_target_languages` to map specific rows to PT-BR or EN-US before writing the results back to your database.

DeepL MCP Server Markup Handling

Corporate wikis rely on formatted HTML. Running `translate_html_markup` ensures your tags survive the trip between languages. Agent pipelines burn through quotas fast. They can hit `get_api_usage` mid-execution to check character limits via this MCP Server.

Audience-Specific Tone Generation

Enterprise clients expect a professional voice. The `translate_text_formal` tool forces business-appropriate grammar rules across supported languages. Social media channels need a different approach. Invoking `translate_text_informal` shifts the output to conversational phrasing, while `get_account_glossaries` ensures your brand terminology stays consistent either way.

Setup guide

Set up DeepL MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with DeepL tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="DeepL_agent",
    model="gemini-2.0-flash",
    instruction="You have access to DeepL tools via MCP.",
    tools=mcp_tools,
)

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Common questions about DeepL MCP in Google ADK

Install `google-adk` via pip. Initialize `McpToolset` with your Vinkius MCP Server endpoint and pass it into the `LlmAgent` under the `tools` parameter.

The agent reads your custom terms using `get_glossary_dictionary`. It applies those exact mappings during the translation process.

Call `get_source_languages` to see what the API accepts. The target list includes specific regional variations.

They absolutely do. The agent checks `get_api_usage` to see your current character constraints before running heavy translation batches.

Vinkius runs this connection in a zero-trust sandbox. The text strings and HTML nodes you translate vanish the moment the session ends, leaving no stored data behind.

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