How to Use the DeepL MCP in CrewAI

Q: How do I connect DeepL tools to a CrewAI agent?

Pass the Vinkius URL directly in your Agent's mcps array. CrewAI will automatically discover tools like translatetext and make them available to that specific agent.

Q: Can a CrewAI moderator agent manage DeepL glossaries?

Yes. By assigning tools like createglossary and deleteglossary to a specific glossary moderator agent, you allow them to update translation terms independently of your translator agents.

Q: How do CrewAI agents select the right DeepL translation tone?

You can configure your translator agent to use translateformal for enterprise clients and translateinformal for consumer channels. The agent reads the task description and invokes the correct tool automatically.

Q: How do I limit which translation tools a CrewAI agent can use?

Use the MCPServerHTTP class from crewai.mcp and define a toolfilter. This lets you restrict access, ensuring a research agent cannot accidentally call deleteglossary.

Q: Are the documents sent to this server secure?

Yes. Document files processed via getdocumentstatus are handled entirely within an ephemeral V8 Isolate Sandbox. Vinkius operates a zero-trust model, meaning your document data is wiped from memory as soon as the translation completes.

Deploy specialized translation crews in CrewAI to localize content, manage glossaries, and verify tone autonomously.

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Connect DeepL MCP to CrewAI

Create your Vinkius account to connect DeepL to CrewAI 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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Setup DeepL with CrewAI

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Multi-agent translation pipelines in CrewAI

The `translate_text` tool acts as the core engine for your localization crew. You assign one agent to write copy, a second agent to run translations using this tool, and a third agent to verify the output against local cultural norms. This MCP setup keeps your division of labor efficient and output quality high. Your agents pass context down the line. The translation agent uses `translate_with_glossary` to ensure industry-specific terms remain accurate, while the editor agent checks for flow and readability before final delivery.

Autonomous glossary curation and maintenance

The `list_glossaries` tool allows a specialized dictionary moderator agent to inspect your existing translation rules. If terms conflict, the agent can call `get_glossary_entries` to analyze the specific language pairs. This keeps your translation assets accurate without manual editing. When outdated terms are flagged, the moderator agent uses `delete_glossary` to remove them. This autonomous maintenance cycle ensures your translation crew always works with fresh, approved dictionaries.

Dynamic language pair discovery

The `list_source_languages` and `list_target_languages` tools let your research agents discover supported translation routes before starting a job. The MCP client checks these lists to verify if a document can be translated directly or if it needs a bridge language. This prevents execution errors during large runs. Once the route is confirmed, the agent hands the task to the translator agent. This pre-flight check makes your autonomous translation crews highly resilient when dealing with diverse global files.

Setup guide

Set up DeepL MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke DeepL tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="DeepL Analyst",
    goal="Access and analyze DeepL data via MCP.",
    backstory="Expert analyst with direct DeepL access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent DeepL transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="DeepL Analyst",
        goal="Access and analyze DeepL data via MCP.",
        backstory="Expert analyst with direct DeepL access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent DeepL transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DeepL. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about DeepL MCP in CrewAI

Pass the Vinkius URL directly in your Agent's `mcps` array. CrewAI will automatically discover tools like `translate_text` and make them available to that specific agent.

Yes. By assigning tools like `create_glossary` and `delete_glossary` to a specific glossary moderator agent, you allow them to update translation terms independently of your translator agents.

You can configure your translator agent to use `translate_formal` for enterprise clients and `translate_informal` for consumer channels. The agent reads the task description and invokes the correct tool automatically.

Use the `MCPServerHTTP` class from `crewai.mcp` and define a `tool_filter`. This lets you restrict access, ensuring a research agent cannot accidentally call `delete_glossary`.

Yes. Document files processed via `get_document_status` are handled entirely within an ephemeral V8 Isolate Sandbox. Vinkius operates a zero-trust model, meaning your document data is wiped from memory as soon as the translation completes.

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