How do I start a transcription for a public video?

Use the `upload_media_for_transcription` tool and provide the public `file_url` along with the source language code (e.g., 'en' for English).

Can I choose which AI voice to use for dubbing?

Yes, first use the `list_available_ai_voices` tool to find a voice ID that matches your target language and gender, then provide it to the `generate_ai_voiceover` tool.

What formats can I export my transcripts in?

The `export_transcription_results` tool supports formats such as 'srt', 'vtt', 'txt', 'pdf', and 'json'.

Maestra MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Maestra
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Maestra through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Maestra "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Maestra?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Pydantic AI validates every Maestra tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Maestra MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Maestra MCP Server

Pydantic AI provides unique advantages when paired with Maestra through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Maestra integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Maestra connection logic from agent behavior for testable, maintainable code

Maestra + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Maestra MCP Server delivers measurable value.

Type-safe data pipelines: query Maestra with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Maestra tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Maestra and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Maestra responses and write comprehensive agent tests

Maestra MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Maestra to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Maestra to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Maestra + Pydantic AI FAQ

Common questions about integrating Maestra MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Maestra MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.