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 CrewAI 8 tools — connect in under 2 minutes

Name: Maestra
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Maestra through Vinkius, pass the Edge URL in the `mcps` parameter and every Maestra tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Maestra Specialist",
    goal="Help users interact with Maestra effectively",
    backstory=(
        "You are an expert at leveraging Maestra tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Maestra "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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 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.

When paired with CrewAI, Maestra becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Maestra tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Maestra MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 8 tools from Maestra

Why Use CrewAI with the Maestra MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Maestra through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Maestra + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Maestra for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Maestra, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Maestra tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Maestra against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Maestra MCP Tools for CrewAI (8)

These 8 tools become available when you connect Maestra to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Maestra + CrewAI FAQ

Common questions about integrating Maestra MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.