DeepL MCP Server for LlamaIndexGive LlamaIndex instant access to 14 tools to Create Glossary, Delete Glossary, Get Document Status, and more
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 App Connector for LlamaIndex
The DeepL app connector for LlamaIndex is a standout in the Ai Frontier category — giving your AI agent 14 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 14 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
Connect your DeepL account to any AI agent and access neural machine translation through natural conversation.
LlamaIndex agents combine DeepL tool responses with indexed documents for comprehensive, grounded answers. Connect 14 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 Translation — Translate text into 30+ languages with optional formality control (formal, informal, or default)
- Glossary-Powered Translation — Apply custom glossaries to ensure consistent terminology across translations
- Glossary Management — Create, list, inspect, and delete custom glossaries with TSV term pairs
- Language Discovery — List all supported source and target languages, and glossary language pair combinations
- API Usage Monitoring — Track character count consumed, remaining quota, and billing period
- Document Translation — Monitor the progress of submitted document translations
The DeepL MCP Server exposes 14 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.
All 14 DeepL tools available for LlamaIndex
When LlamaIndex connects to DeepL through Vinkius, your AI agent gets direct access to every tool listed below — spanning machine-translation, language-processing, glossary-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a glossary
Delete a glossary
Check document translation status
Get glossary details
Get glossary entries
Check API usage
List glossaries
List glossary language pairs
List source languages
List target languages
Translate with formal tone
Translate with informal tone
Translate text
Translate using glossary
Connect DeepL to LlamaIndex via MCP
Follow these steps to wire DeepL into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
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
Example Prompts for DeepL in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with DeepL immediately.
"Translate 'Welcome to our platform. We look forward to working with you.' into German (formal) and Brazilian Portuguese (informal)."
"Create a glossary for EN→FR with our brand terms and then translate a marketing paragraph using it."
"Check my DeepL API usage and list all available target languages."
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.