Maestra MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Maestra 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 Maestra. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Maestra?"
)
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 Maestra MCP Server
Connect your Maestra.ai account to any AI agent to automate your media processing workflows. This MCP server enables your agent to upload audio/video files for transcription, translate transcripts into 125+ languages, and generate synthetic AI voiceovers directly from natural language interfaces.
LlamaIndex agents combine Maestra tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Automated Transcription — Upload media files via public URLs and receive accurate, speaker-aware transcripts instantly
- Global Translation — Translate existing transcriptions into over 125 different languages to reach a worldwide audience
- AI Dubbing — Generate high-quality synthetic voiceovers for your media using a wide range of available AI voices
- Asset Management — List all files in your account, monitor processing statuses, and organize content into folders
- Result Export — Generate temporary download links for results in formats like SRT, VTT, PDF, and JSON
The Maestra MCP Server exposes 8 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 Maestra to LlamaIndex via MCP
Follow these steps to integrate the Maestra 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 8 tools from Maestra
Why Use LlamaIndex with the Maestra MCP Server
LlamaIndex provides unique advantages when paired with Maestra through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Maestra tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Maestra tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Maestra, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Maestra tools were called, what data was returned, and how it influenced the final answer
Maestra + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Maestra MCP Server delivers measurable value.
Hybrid search: combine Maestra real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Maestra 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 Maestra for fresh data
Analytical workflows: chain Maestra queries with LlamaIndex's data connectors to build multi-source analytical reports
Maestra MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Maestra to LlamaIndex via MCP:
export_transcription_results
Get an export link for a processed file
generate_ai_voiceover
Generate a synthetic voiceover for a media file
get_file_details
Get details and status for a specific file
list_account_folders
List all folders in your account
list_available_ai_voices
List all available synthetic AI voices
list_maestra_files
List all audio and video files in your Maestra account
translate_transcription
Translate an existing transcription into a new language
upload_media_for_transcription
Requires a public file URL and target source language. Upload a new file for transcription
Example Prompts for Maestra in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Maestra immediately.
"Upload the video at 'https://example.com/video.mp4' for English transcription in Maestra."
"List all available AI voices for French."
"Get an SRT export link for file ID 'vid-12345'."
Troubleshooting Maestra MCP Server with LlamaIndex
Common issues when connecting Maestra to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMaestra + LlamaIndex FAQ
Common questions about integrating Maestra 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 Maestra 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 Maestra to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.