How do I find my DeepL API Key?

Log in to your [**DeepL account**](https://www.deepl.com/pro-account/plan), and you will find your Authentication Key under the 'Account' tab. Copy and paste it below.

Does this work with both Free and Pro API keys?

Yes. This server automatically detects if you are using a Free key (ending in `:fx`) or a Pro key and routes requests to the correct endpoint.

Can the agent use glossaries?

Yes. You can list your existing glossaries via the `get_glossaries` tool and identify their IDs to maintain consistency in your agent-led translations.

DeepL MCP Server for CrewAI 9 tools — connect in under 2 minutes

Name: DeepL
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 9 Tools Framework

Connect your CrewAI agents to DeepL through the Vinkius — pass the Edge URL in the `mcps` parameter and every DeepL tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="DeepL Specialist",
    goal="Help users interact with DeepL effectively",
    backstory=(
        "You are an expert at leveraging DeepL tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in DeepL "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 9 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About DeepL MCP Server

Empower your AI agent to orchestrate your entire multilingual workflow with DeepL, the world's most accurate AI translator. By connecting DeepL to your agent, you transform complex translation tasks into a natural conversation. Your agent can instantly translate text between dozens of languages, audit available language pairs, and monitor API usage without you ever touching a technical dashboard. Whether you are localized content or communicating with international teams, your agent acts as a real-time linguistic bridge, ensuring your communication is always precise and professional.

When paired with CrewAI, DeepL becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call DeepL tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Text Auditing — Translate text into target languages and retrieve detected source language metadata instantly.
Linguistic Oversight — List all supported source and target languages to maintain a clear view of translation options.
Usage Intelligence — Monitor your character count and API limits to maintain strict control over your translation budget.
Glossary Management — List and query configured translation glossaries to ensure consistent brand terminology.
Contextual Tone Control — Translate text enforcing strict formal, informal, or standard business tones instantly.
Markup Preservation — Translate HTML elements while safely preserving tag boundaries and web structure.

The DeepL MCP Server exposes 9 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect DeepL to CrewAI via MCP

Follow these steps to integrate the DeepL MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 9 tools from DeepL

Why Use CrewAI with the DeepL MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with DeepL through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

DeepL + CrewAI Use Cases

Practical scenarios where CrewAI combined with the DeepL MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries DeepL for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries DeepL, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain DeepL tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries DeepL against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

DeepL MCP Tools for CrewAI (9)

These 9 tools become available when you connect DeepL to CrewAI via MCP:

get_account_glossaries

List configured translation glossaries

get_api_usage

Get current API usage and character limit constraints

get_glossary_dictionary

Get term mapping entries for a specific glossary ID

get_source_languages

List all supported source languages for translation

get_target_languages

g., EN-US, PT-BR) that DeepL can translate TO. List all supported target languages for translation

translate_html_markup

Translate HTML elements while preserving tag structure

translate_text_formal

g., "Sie" in German, "vous" in French) suitable for business communications. Translate text using a formal/business tone

translate_text_informal

g., "du" in German, "tu" in French) suitable for casual platforms. Translate text using an informal/casual tone

translate_text_standard

Translate text into a target language using standard tone

Example Prompts for DeepL in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with DeepL immediately.

"Translate 'Hello world' into Portuguese using DeepL."

"Show me all supported target languages in DeepL."

"What is my current DeepL usage?"

Troubleshooting DeepL MCP Server with CrewAI

Common issues when connecting DeepL to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

DeepL + CrewAI FAQ

Common questions about integrating DeepL MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.