What kind of Lokalise API token do I need for this integration?

You need a Personal Access Token from your Lokalise account. Go to your profile settings, find the API Tokens section, and generate a new token with read/write access to projects. Keep this token secure as it grants full API access to your account.

How do I add a new translation key to my Lokalise project via the AI agent?

Use the `create_key` action with the project ID, key name (e.g., `homepage.welcome.title`), and optionally specify platforms, tags, and a description. The key will be created with the base language placeholder ready for translation.

Can I export all my translations in a specific format like JSON or YAML?

Yes! Use the `download_file` action and specify the `format` parameter (e.g., `json`, `yml`, `xliff`, `strings`, `xml`). You can also filter by specific languages or filenames to generate targeted export bundles.

Is it possible to add translations programmatically for multiple languages at once?

Yes. The `add_translation` action supports adding translations for one or more keys at once. You can also mark translations as fuzzy or reviewed to indicate their quality status in the workflow.

Lokalise MCP Server for LlamaIndex 13 tools — connect in under 2 minutes

Name: Lokalise
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 13 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Lokalise as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Lokalise. "
            "You have 13 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Lokalise?"
    )
    print(response)

asyncio.run(main())

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* 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 Lokalise MCP Server

Connect your Lokalise account to any AI agent and take full control of your translation and localization workflows through natural conversation.

LlamaIndex agents combine Lokalise tool responses with indexed documents for comprehensive, grounded answers. Connect 13 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

Project Management — List all translation projects, fetch detailed project metadata, and create new projects directly from the API
Key Management — Query translation keys with filters by platform, tags, or filenames, plus create and update keys programmatically
Translation Operations — Fetch translations for any key, add new translations with review/fuzzy flags, and manage multi-language content
File Import/Export — Upload localization files (JSON, YAML, XLIFF) and generate download bundles in any supported format
Team & Orders — List team members and their roles, plus inspect professional translation orders

The Lokalise MCP Server exposes 13 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 Lokalise to LlamaIndex via MCP

Follow these steps to integrate the Lokalise 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 13 tools from Lokalise

Why Use LlamaIndex with the Lokalise MCP Server

LlamaIndex provides unique advantages when paired with Lokalise through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Lokalise tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Lokalise tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Lokalise, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Lokalise tools were called, what data was returned, and how it influenced the final answer

Lokalise + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Lokalise MCP Server delivers measurable value.

Hybrid search: combine Lokalise real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Lokalise 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 Lokalise for fresh data

Analytical workflows: chain Lokalise queries with LlamaIndex's data connectors to build multi-source analytical reports

Lokalise MCP Tools for LlamaIndex (13)

These 13 tools become available when you connect Lokalise to LlamaIndex via MCP:

add_translation

Add translations to one or more keys

create_key

Create translation keys in a Lokalise project

create_project

Create a new Lokalise project

download_file

Generate a download bundle of translations

get_project

Get details of a specific Lokalise project

list_keys

List translation keys in a Lokalise project

list_languages

List languages in a Lokalise project

list_orders

List translation orders in your Lokalise account

list_projects

List all Lokalise projects

list_team_members

List all team members in your Lokalise account

list_translations

List translations for a key in a Lokalise project

update_key

Update an existing translation key

upload_file

Upload a localization file to a Lokalise project

Example Prompts for Lokalise in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Lokalise immediately.

"List all my Lokalise translation projects and show their current status."

"Create a new translation key 'checkout.success.message' in my Web App project for the web platform."

"Download all Portuguese (pt-BR) translations from my Mobile App project in JSON format."

Troubleshooting Lokalise MCP Server with LlamaIndex

Common issues when connecting Lokalise to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Lokalise + LlamaIndex FAQ

Common questions about integrating Lokalise MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Lokalise tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.