Maestra MCP. Automate Transcription, Translation, and Voiceovers.
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Maestra MCP Server automates your entire media lifecycle: uploading videos, transcribing speech into text, translating that text into over 125 languages, and generating professional synthetic voiceovers.
Your agent handles everything from listing content assets to exporting the final SRT or VTT file.
What your AI agents can do
Export transcription results
Generates a temporary download link for the final processed media data, like an SRT or VTT file.
Generate ai voiceover
Creates synthetic audio by reading text and generating a professional-sounding voiceover.
Get file details
Retrieves the current status, size, and metadata for any specific file ID in your account.
Your agent sends a public URL to the server, starting the process of converting video or audio into text.
The agent takes a finished transcript and converts it into an entirely new language using translate_transcription.
Using the generated text, your agent calls generate_ai_voiceover to create high-quality audio dubs.
The server allows you to check all files (list_maestra_files) or folders (list_account_folders) within your Maestra account for status checks.
Your agent requests an export link, providing the processed data as SRT, VTT, PDF, or JSON files using export_transcription_results.
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Maestra MCP Server: 8 Tools for Media Pipelines
These tools allow your agent to manage every step of the media lifecycle, from uploading raw files to exporting final translated and dubbed content.
019d75cbexport transcription results
Generates a temporary download link for the final processed media data, like an SRT or VTT file.
019d75cbgenerate ai voiceover
Creates synthetic audio by reading text and generating a professional-sounding voiceover.
019d75cbget file details
Retrieves the current status, size, and metadata for any specific file ID in your account.
019d75cblist account folders
Returns a list of all folder names you've set up to organize your media assets.
019d75cblist available ai voices
Checks and lists every synthetic AI voice available for selection when creating a voiceover.
019d75cblist maestra files
Outputs an index of all audio and video files currently stored in your Maestra account.
019d75cbtranslate transcription
Converts a completed text transcript from one language into another target language.
019d75cbupload media for transcription
Uploads an external media file via URL and specifies the source language to begin transcription.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Maestra, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
You connect your agent to Maestra MCP Server to handle your whole media workflow, from raw footage to polished, dubbed content. You don't lift a finger; your agent does the heavy lifting.
To start, you can manage your assets by running list_account_folders which gives you every folder name you’ve set up for organization, or use list_maestra_files to get an index of every single audio and video file sitting in your Maestra account. If you gotta check the status, size, or metadata on a specific piece of content, just run get_file_details with the file ID.
When you're ready for transcription, your agent sends a public URL to the server using upload_media_for_transcription, making sure you specify the source language. It then converts that video or audio into text transcripts. Once that transcript is done and sitting there, you can run translate_transcription to convert it into an entirely new target language among over 125 options.
If you need high-quality dubbing, you first check out the available voices by running list_available_ai_voices. Then, using the translated or original text, your agent calls generate_ai_voiceover to create professional synthetic audio. This gives you a full voice track for your content.
When everything's done—the transcription, the translation, and the dubbing—your final stop is requesting an export link. You run export_transcription_results, which spits out temporary download links containing all the processed data in formats like SRT, VTT, PDF, or JSON files.
How Maestra MCP Works
- 1 First, your agent uses
upload_media_for_transcriptionby providing a public URL and specifying the source language. The server starts processing. - 2 Next, you can use
translate_transcriptionto convert the resulting English transcript into Spanish or Japanese, for example. - 3 Finally, your agent calls
generate_ai_voiceoverusing the translated text to create the final dubbing audio file.
The bottom line is that Maestra takes a source media file and turns it through transcription, translation, and voice generation in one flow.
Who Is Maestra MCP For?
Content creators who need to quickly localize videos. Localization teams managing global marketing campaigns. Developers building applications around multi-lingual media processing. Anyone whose job involves taking a single piece of recorded content and needing it in dozens of languages.
You upload the master video file, let your agent handle the transcription, then use translate_transcription to get subtitles for 10 different markets.
You need a full campaign rollout. You list all assets with list_maestra_files, translate them in batches using translate_transcription, and then manage the voiceover process via generate_ai_voiceover.
You build a microservice that must ingest media, transcribe it for metadata, and save the results. You'll rely heavily on file status checks using get_file_details.
What Changes When You Connect
- It saves time by automating the whole chain. Instead of manually transcribing a video, then copy-pasting that text into a translator, your agent handles
upload_media_for_transcription->translate_transcription->generate_ai_voiceoverin one sequence. - You always know what you're dealing with. Use
list_maestra_filesand then check the status for any file ID usingget_file_details. No guesswork about whether a job finished or failed. - The output is structured, not messy. When the process finishes, your agent requests an export link via
export_transcription_results, giving you clean SRT, VTT, or JSON files ready to use. - You're never stuck on language choice. Before generating a voiceover, check out every option available using
list_available_ai_voicesso you can pick the perfect tone and gender for your project. - It keeps everything organized. You don't lose tracks of content. Use
list_account_foldersto see where all your regional assets are stored within Maestra.
Real-World Use Cases
The Global Marketing Campaign
A client needs a single product video localized for 12 different countries. They use their agent to first run upload_media_for_transcription on the master file, then iterate through all target languages using translate_transcription. Finally, they loop through the results calling generate_ai_voiceover until every asset has a perfectly dubbed version.
The Conference Booth Demo
A developer needs to show how their system handles multi-lingual data. They use their agent to run list_maestra_files to confirm all source assets are present, then pick a file and check its status with get_file_details before triggering the full translation cycle.
The Podcast Archive Cleanup
A podcast producer has 50 hours of raw audio. They use their agent to process each hour via upload_media_for_transcription. Once all are transcribed, they then call list_available_ai_voices and generate a consistent voiceover for the entire archive using generate_ai_voiceover.
The Developer Sandbox Test
A developer wants to test their new file-download feature. They upload a dummy video, wait for it to process, and then use export_transcription_results immediately to confirm they get the expected SRT format link.
The Tradeoffs
Trying to translate without transcribing first
The user thinks they can just pass a raw video URL to translate_transcription. The server won't work because translation requires source text, not media.
→
You must run the content through upload_media_for_transcription first. That step creates the necessary text data that translate_transcription needs as input.
Assuming a single tool handles everything
The user tries to combine transcription, translation, and voiceover in one call. This fails because these are three distinct, sequential processes.
→
You need a multi-step sequence: Start with upload_media_for_transcription, then use translate_transcription on the result, and finally call generate_ai_voiceover.
Forgetting to check file status
The user calls a tool expecting an immediate output but gets an error because the previous media processing job is still running in the background.
→
Always use get_file_details after initiating a large process. This checks the current state of the file, telling you if it's 'Processing', 'Ready', or 'Failed'.
When It Fits, When It Doesn't
Use this Maestra server if your primary need is transforming recorded media (video/audio) into structured, multi-lingual text and then back into dubbing audio. Specifically, if you require a workflow that moves from raw asset -> transcript -> translation -> voiceover, this is built for it. Don't use it if your problem is simply editing video footage or adding visual effects; Maestra handles the data pipeline. Also, don't rely on it for real-time live captioning—it processes uploaded files. If you only need to manage assets and check status without doing any processing, list_maestra_files alone might be enough, but using the full server gives you all the options.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Maestra. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Dealing with global content is a mess of spreadsheets and manual copy-pasting.
Before Maestra, localizing video was painful. You'd record a master file, then manually download the transcript (often messy or incomplete). You’d paste that text into Google Translate, downloading separate files for each language. Then, you had to find a voiceover service and re-record every single segment—a process that took days and cost thousands.
Now, your agent handles it all. Start by calling `upload_media_for_transcription` with the master file. Once transcribed, use `translate_transcription` to generate 125+ languages instantly. Finally, one call to `generate_ai_voiceover` creates a perfect dubbing track—no more manual transfers or messy files.
Maestra MCP Server: Get full media automation from chat.
The specific steps that vanish are the file downloads, the language code lookups, and the individual API calls for each step. You don't need to write complex orchestration logic; your agent just needs three simple commands: upload, translate, dub.
What’s different now is the speed and consistency. Instead of treating translation and voiceover as separate services that require manual handoffs, Maestra keeps them in one reliable pipeline. It takes a single media asset and turns it into a finished, multi-lingual product.
Common Questions About Maestra MCP
How do I start the process using upload_media_for_transcription? +
You need to provide a public URL for your video or audio file and specify the source language. The server then begins processing it, generating an initial transcript.
What is the difference between transcribe_transcription and translate_transcription? +
Transcription converts media (audio/video) into text. Translation takes existing text and converts its language (e.g., English to French). You must transcribe first.
Can I see all the available voices for generate_ai_voiceover? +
Yes, call list_available_ai_voices. This tool returns a full list of synthetic voices and their characteristics so you can pick one that matches your brand.
If I translate the text, how do I get the final file? +
After running translation or voiceover generation, use export_transcription_results. This gives you a temporary download link for the finished content in formats like SRT or VTT.
How do I check if my large media job is still working? +
Use get_file_details with the file ID. It tells you the current processing status (e.g., 'Pending', 'Processing', or 'Complete').
When using `list_maestra_files`, what file formats and codecs does the Maestra server support for uploads? +
The server supports common media types including MP4, MOV, WAV, and FLAC files. While it handles most standard audio/video codecs, complex or highly compressed proprietary formats may require pre-conversion to ensure processing success.
If my job fails during transcription, how do I use `get_file_details` to diagnose the specific error? +
You must pass the unique file ID into get_file_details. This tool returns a detailed status object that includes failure codes and descriptive messages. These codes tell you if the issue was due to format incompatibility or API permission errors.
How can I use `list_account_folders` to keep my translated media assets organized? +
You first run list_account_folders to see your current structure. You then use the agent's capability to create new folders for specific projects (e.g., 'Global Campaign Q3'). This keeps all related transcription and voiceover results grouped logically.
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'.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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