Crowdin MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Crowdin 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 Crowdin. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Crowdin?"
)
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 Crowdin MCP Server
Integrate Crowdin, the leading localization management platform, directly into your AI workflow. Manage your translation projects, monitor file statuses, and track localization tasks using natural language.
LlamaIndex agents combine Crowdin tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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 and retrieve detailed settings and statuses for all your localization projects.
- File Operations — Monitor files within projects and retrieve specific file metadata.
- Task & Workflow Tracking — Track translation and proofreading tasks to ensure timely delivery.
- Resource Insights — Access glossaries, translation memories, and supported language lists via chat.
The Crowdin MCP Server exposes 10 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 Crowdin to LlamaIndex via MCP
Follow these steps to integrate the Crowdin 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 10 tools from Crowdin
Why Use LlamaIndex with the Crowdin MCP Server
LlamaIndex provides unique advantages when paired with Crowdin through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Crowdin tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Crowdin tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Crowdin, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Crowdin tools were called, what data was returned, and how it influenced the final answer
Crowdin + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Crowdin MCP Server delivers measurable value.
Hybrid search: combine Crowdin real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Crowdin 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 Crowdin for fresh data
Analytical workflows: chain Crowdin queries with LlamaIndex's data connectors to build multi-source analytical reports
Crowdin MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Crowdin to LlamaIndex via MCP:
get_file_details
Touches file structure, revision history, and per-language translation status boundaries. Get metadata for a specific file in a project
get_project_details
Touches source/target language settings and project-level activity summary boundaries. Get detailed settings and status for a project
list_glossaries
Resolves glossary names, IDs, and language pairs used for terminology management. List all glossaries available in the account
list_project_files
Resolves file names, IDs, paths, and current translation progress metrics. List all files within a specific project
list_project_reports
Resolves report names, types (Translation Costs, Progress), and creation timestamps. List generated reports for a specific project
list_project_screenshots
Resolves screenshot IDs, tags, and linked string identifiers used for visual context. List all screenshots uploaded to a project for context
list_project_tasks
Resolves task titles, types (Translation, Proofreading), status, and assigned linguist references. List translation and proofreading tasks for a project
list_projects
Resolves project names, IDs, source languages, and target languages for localization workflows. List all localization projects in your Crowdin account
list_supported_languages
Resolves language codes, human-readable names, and locale identifiers. List all languages supported by Crowdin
list_translation_memories
Resolves TM names, IDs, and segment counts for reuse in future translations. List all translation memories (TMs) available
Example Prompts for Crowdin in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Crowdin immediately.
"List all localization projects in my account."
"What is the status of files in project 'Mobile App'?"
"List all active translation tasks for my projects."
Troubleshooting Crowdin MCP Server with LlamaIndex
Common issues when connecting Crowdin to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCrowdin + LlamaIndex FAQ
Common questions about integrating Crowdin 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 Crowdin 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 Crowdin to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.