How to Use the DeepL MCP in Mastra AI

Q: Can I require human approval for DeepL translations in Mastra AI?

Yes. Use the requireToolApproval option when registering translatehtmlmarkup. This pauses the workflow until a human reviews the translated HTML.

Q: How do I sync glossary terms between DeepL and Mastra AI?

Your workflow can call getglossarydictionary at the start of a run. This fetches the mapping and caches it within your Mastra AI execution context.

Q: Can I monitor translation volume programmatically?

Yes, call getapiusage inside a scheduled workflow step. You can trigger Slack alerts if your character consumption approaches your monthly cap.

Q: Where are my DeepL glossary terms stored during execution?

Glossary mappings fetched by getglossarydictionary remain in your local Mastra AI instance memory. No data is cached on Vinkius servers.

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Connect DeepL MCP to Mastra AI

Create your Vinkius account to connect DeepL to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Automate fallback translation steps in Mastra AI

`translate_text_standard` translates your system notifications inside Mastra AI workflow steps. If a translation step fails due to API limits, the workflow engine automatically retries with exponential backoff. You configure conditional branching based on the output language. The agent checks `get_source_languages` first, routes the payload to the correct localization queue, and continues execution without manual intervention.

Enforce corporate terminology in automated workflows

`get_glossary_dictionary` pulls your pre-defined brand translations directly into Mastra AI agent steps. This ensures that automated email campaigns preserve your product names across regional markets. Your agent calls `get_account_glossaries` to verify which dictionaries are active before running bulk updates. You prevent translation errors from reaching production without having to write custom validation scripts.

Tone-sensitive routing via Mastra AI MCP Server

`translate_text_formal` routes customer support tickets through a formal communication path when dealing with enterprise clients. Mastra AI evaluates the client tier, then selects this specific tool to keep the tone professional. For casual channels, the workflow switches to `translate_text_informal` dynamically. This MCP Server setup handles tone selection programmatically based on the metadata in your workflow context.

Setup guide

Set up DeepL MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All DeepL tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "deepl-mcp-client",
  servers: {
    "deepl-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "DeepL Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to DeepL tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent DeepL transactions"
);
console.log(result.text);

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Common questions about DeepL MCP in Mastra AI

Mastra AI uses its built-in workflow engine to retry failed calls to `translate_text_standard` automatically. You define the backoff strategy in your agent configuration.

Yes. Use the `requireToolApproval` option when registering `translate_html_markup`. This pauses the workflow until a human reviews the translated HTML.

Your workflow can call `get_glossary_dictionary` at the start of a run. This fetches the mapping and caches it within your Mastra AI execution context.

Yes, call `get_api_usage` inside a scheduled workflow step. You can trigger Slack alerts if your character consumption approaches your monthly cap.

Glossary mappings fetched by `get_glossary_dictionary` remain in your local Mastra AI instance memory. No data is cached on Vinkius servers.

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