How to Use the Localazy (AI Translation & Localization API) MCP in LlamaIndex

Q: How do I integrate the Localazy (AI Translation & Localization API) with LlamaIndex?

Install llama-index-tools-mcp and initialize the client with your Vinkius MCP server URL. Convert the tools using McpToolSpec and pass them to your FunctionAgent.

Q: Can I search my Localazy files semantically using LlamaIndex?

Yes. You can use listfilecontent to pull all keys and embed them into a vector index for semantic search.

Q: How does the Localazy (AI Translation & Localization API) MCP Server handle large translation files in LlamaIndex?

The downloadfile tool retrieves raw content efficiently. Your LlamaIndex pipeline can chunk this data before indexing to avoid hitting token limits.

Q: Can my agent manage webhooks through this integration?

Yes, the agent can use listwebhooks and updatewebhooks to monitor project changes. This lets you trigger re-indexing pipelines whenever translations are updated.

Build RAG pipelines that index your Localazy translation data and manage keys directly from LlamaIndex.

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Connect Localazy (AI Translation & Localization API) MCP to LlamaIndex

Create your Vinkius account to connect Localazy (AI Translation & Localization API) 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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Setup Localazy (AI Translation & Localization API) with LlamaIndex

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Indexing localization databases

This MCP server provides the `list_file_content` tool to pull raw keys and translations into your LlamaIndex vector store. Your agent can query past translations semantically, ensuring new copy matches the tone of older releases. By indexing files retrieved with `list_files`, you build a searchable knowledge base of your entire localization history. Preventing duplicate keys and conflicting translations becomes automatic.

Contextual glossary retrieval

The `list_glossary_terms` tool lets your LlamaIndex agent fetch active project glossaries on the fly. When generating new UI copy, your agent queries this glossary to ensure it uses approved terminology. If a term is missing, the agent triggers `create_glossary_term` to add it. This tightly couples your generation pipeline with your actual localization rules.

Automated project audit via LlamaIndex

The `list_projects` tool is the entry point for your agent to analyze active localization statistics across your organization. It queries project health, language counts, and key completion rates to build status reports. Combined with `list_cdn_metadata`, your agent can verify which translation files are live on the CDN. You get immediate alerts if a critical language is missing from the latest release.

Setup guide

Set up Localazy (AI Translation & Localization API) 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 Localazy (AI Translation & Localization API) 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 Localazy (AI Translation & Localization API) tools.",
)
response = await agent.run("List recent Localazy (AI Translation & Localization API) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Localazy. 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 Localazy (AI Translation & Localization API) MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the client with your Vinkius MCP server URL. Convert the tools using `McpToolSpec` and pass them to your `FunctionAgent`.

Yes. You can use `list_file_content` to pull all keys and embed them into a vector index for semantic search.

The `download_file` tool retrieves raw content efficiently. Your LlamaIndex pipeline can chunk this data before indexing to avoid hitting token limits.

Yes, the agent can use `list_webhooks` and `update_webhooks` to monitor project changes. This lets you trigger re-indexing pipelines whenever translations are updated.

The MCP Server processes your translation keys and files ephemerally. Vinkius uses zero-trust, isolated environments, meaning your localization source keys are never cached or stored on the hosting platform.

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