DeepL MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add DeepL as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to DeepL. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in DeepL?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine DeepL tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the DeepL MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 9 tools from DeepL
Why Use LlamaIndex with the DeepL MCP Server
LlamaIndex provides unique advantages when paired with DeepL through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine DeepL tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain DeepL tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query DeepL, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what DeepL tools were called, what data was returned, and how it influenced the final answer
DeepL + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the DeepL MCP Server delivers measurable value.
Hybrid search: combine DeepL real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query DeepL to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying DeepL for fresh data
Analytical workflows: chain DeepL queries with LlamaIndex's data connectors to build multi-source analytical reports
DeepL MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect DeepL to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting DeepL to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDeepL + LlamaIndex FAQ
Common questions about integrating DeepL MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect DeepL with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect DeepL to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.