How to Use the DeepL MCP in LlamaIndex

Q: How do I index DeepL translations using LlamaIndex?

Use the MCP tool spec to load translatetext into your LlamaIndex agent. The agent translates foreign text chunks before passing them to your vector store indexer.

Q: Can LlamaIndex query my DeepL glossaries?

Yes, the agent uses getglossaryentries to pull glossary data into your index. This lets your RAG pipeline search your custom terms to ensure translation consistency.

Q: Does this MCP Server support document translation tracking in LlamaIndex?

Yes, your pipeline can call getdocumentstatus to monitor large translation jobs. This ensures you only index fully translated documents into your vector store.

Q: How do I check which languages my index supports?

Call listtargetlanguages to retrieve the active list of target languages. Your agent uses this data to validate queries before attempting translation.

Index DeepL translations directly into your LlamaIndex vector stores for semantic retrieval.

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Create your Vinkius account to connect DeepL to LlamaIndex 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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Index translations in LlamaIndex

This MCP Server uses `translate_text` to translate incoming documents before indexing them into your vector store. Your LlamaIndex pipeline translates foreign language queries, searches the English index, and translates the retrieved nodes back to the user's native tongue. By combining translation with vector search, you create a multilingual knowledge base that works across 30+ languages. The agent handles the translation step automatically, ensuring your index remains grounded and free of hallucinations.

Sync DeepL glossaries with LlamaIndex

The `get_glossary_entries` tool extracts your active translation rules so your pipeline can index them for semantic search. This allows your RAG application to query past translation decisions and maintain terminology alignment across different language indexes. You can also use `list_glossary_language_pairs` to verify which language combinations have active glossaries. This metadata is stored alongside your indexed documents, ensuring your retrieval pipeline knows exactly how terms were mapped.

Check translation jobs in LlamaIndex

The `get_document_status` tool tracks the progress of large document translation tasks directly from your ingestion pipeline. Your agent queries this tool to monitor background translation jobs before converting the completed files into searchable index nodes. This approach prevents your RAG system from indexing incomplete or corrupted documents. Your pipeline waits for a successful status signal before committing the translated content to your vector database.

Setup guide

Set up DeepL MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all DeepL MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to DeepL tools.",
)
response = await agent.run("List recent DeepL data")

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Common questions about DeepL MCP in LlamaIndex

Use the MCP tool spec to load `translate_text` into your LlamaIndex agent. The agent translates foreign text chunks before passing them to your vector store indexer.

Yes, the agent uses `get_glossary_entries` to pull glossary data into your index. This lets your RAG pipeline search your custom terms to ensure translation consistency.

Yes, your pipeline can call `get_document_status` to monitor large translation jobs. This ensures you only index fully translated documents into your vector store.

Call `list_target_languages` to retrieve the active list of target languages. Your agent uses this data to validate queries before attempting translation.

Your source documents and translated texts are processed inside a secure V8 isolate sandbox. We do not store your raw translation payloads or glossary entries. Everything is handled ephemerally and cleared immediately after execution.

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