Speechnotes MCP. Start transcribing audio from a simple chat prompt.
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…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Speechnotes connects your AI agent to an industry-standard transcription engine. It handles everything from initiating a job using a remote URL to fetching usage statistics and exporting polished files in TXT, DOCX, or SRT formats.
Manage the full lifecycle of professional audio transcription—monitor status, list history, and control language models, all through natural conversation with your AI client.
What your AI agents can do
Remove transcription job
Deletes a specific transcription job record from your account history.
Get remaining credits
Checks your current API account balance and available transcription credits.
Get transcription export
Downloads the finished transcript text in a specified format (TXT, DOCX, SRT).
Sends an audio file's URL to the server, beginning a new transcription process.
Retrieves real-time progress on running jobs or lists metadata for completed transcriptions.
Downloads the final text result, formatted as TXT, DOCX, or SRT.
Checks remaining API credits and retrieves detailed usage statistics for billing purposes.
Returns a list of supported languages and their corresponding codes for accurate transcription settings.
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Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Speechnotes MCP Server: 12 Tools for Audio Job Management
Use these twelve tools to manage every aspect of transcription, from starting a job and checking status to exporting results and monitoring account usage.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Speechnotes on Vinkius019dd164remove transcription job
Deletes a specific transcription job record from your account history.
019dd164get remaining credits
Checks your current API account balance and available transcription credits.
019dd164get transcription export
Downloads the finished transcript text in a specified format (TXT, DOCX, SRT).
019dd164list transcription models
Lists the different AI models available for running transcriptions.
019dd164generate webhook signature
Creates a cryptographic signature for an outgoing webhook payload.
019dd164get transcription status
Checks the current processing state of a transcription job using its unique ID.
019dd164get usage statistics
Retrieves detailed logs and metrics about how your account has been used over time.
019dd164list transcription history
Retrieves metadata and records for all past or completed transcription jobs.
019dd164list supported languages
Fetches a list of language codes supported by the transcription engine.
019dd164list configured webhooks
Lists all the delivery endpoints you have set up to receive webhook notifications.
019dd164test speechnotes auth
Runs a quick check to verify that the connection and API credentials are working correctly.
019dd164transcribe audio url
Sends an audio file URL to start a new, high-accuracy transcription job.
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
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Start with Speechnotes, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Speechnotes. 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 12 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Managing transcripts shouldn't feel like juggling tabs and dashboards.
Today, transcriptions are a multi-step mess. You drop the audio in one service; you wait for email notifications from another dashboard; then you have to jump into a third tool just to download the correct format (DOCX vs. TXT). Copying data between these apps is slow and error-prone.
With Speechnotes, your agent handles it all in chat. You pass the URL, and the system manages the job status check, the processing queue, and the final retrieval. What you get is a single, reliable conversation thread that ends with the clean text file you need.
Speechnotes MCP Server: Get accurate transcripts in plain chat.
Manually tracking job status means opening multiple browser tabs and refreshing pages repeatedly. If your process fails, you have no central record of what happened or why the transcription stalled.
Now, the agent manages this complexity for you. You tell it to check status using `get_transcription_status`, and it gives you a direct answer. The whole sequence—from start to finish—happens within your chat window.
What you can do with this MCP connector
Yo, you know how much time it sucks to deal with audio files? This server plugs Speechnotes right into your AI agent so you can handle high-accuracy transcription jobs without switching apps or writing a single API call. It manages the whole damn process—from kicking off the job using just an audio URL all the way through pulling out clean, exportable text.
When you use this, your agent handles everything. You send an audio file's URL, and it starts a new transcription job immediately. If you need to see how far along that thing is, you check the current processing state using a unique job ID. When the job's done, you can pull out the final text result, formatted exactly how you want—whether that's plain TXT, polished DOCX, or timed SRT.
Need to keep track of what you sent? You retrieve metadata and records for every past transcription job in one shot. For managing your account, you check your current API balance instantly using get_remaining_credits and pull detailed usage logs with get_usage_statistics. Before you start anything big, the agent can also list all supported language codes so you know what languages the engine handles.
You'll even see a rundown of different AI models available for running transcriptions.
For advanced setup, your agent lets you list every delivery endpoint you've configured to receive webhook notifications and it can generate a cryptographic signature for any outgoing webhook payload. If you need to clean up history or manage job records, you can delete specific transcription jobs using their IDs. And hey, if you wanna know if the connection's solid, running test_speechnotes_auth gives you a quick verification check.
So basically, your agent starts the job from an audio URL; it lets you track status and review history; it exports the final text in TXT, DOCX, or SRT formats; and it handles all the billing checks—credits, usage, models, and languages. It's got the whole lifecycle locked down for ya.
019dd164-d66f-71ba-8c19-0f81de191411 
How Speechnotes MCP Works
- 1 Subscribe to the server and enter your Speechnotes API Key and Secret into your agent's configuration.
- 2 Tell your agent what you need (e.g., 'Transcribe this URL: [link]'). The agent uses
transcribe_audio_urlto initiate the job. - 3 Wait for confirmation or ask the agent to check status using
get_transcription_status. Once complete, request the export viaget_transcription_export.
The bottom line is you talk to your AI client; the client talks to Speechnotes API tools; Speechnotes does the hard work and gives you text back.
Who Is Speechnotes MCP For?
Journalists, content creators, researchers, and operations teams. This is for anyone whose job involves turning spoken word—interviews, meetings, podcasts—into usable written assets fast. It solves the pain of context switching between an audio player, a transcription service, and a document editor.
Transcribes multiple field interviews quickly by feeding URLs into the agent and using list_transcription_history to track everything.
Processes podcast recordings, checks job status with get_transcription_status, and pulls the final transcript as a DOCX for blog posts.
Monitors account spending by calling get_remaining_credits to ensure departments don't run out of transcription capacity mid-week.
What Changes When You Connect
- Handles the full lifecycle. You don't need separate calls for starting, checking, and exporting. Use
transcribe_audio_urlto start, thenget_transcription_statusuntil it finishes, and finallyget_transcription_exportall in one agent sequence. - Maintains clear accountability. Always know your limits. Call
get_remaining_creditsbefore running any large jobs. Also checkget_usage_statisticsto see exactly where bandwidth is going. - Keeps data organized. Need proof of past work? Use
list_transcription_history. You get detailed metadata, timestamps, and speaker counts—it's better than a simple job list. - Supports complex workflows. The server lets you manage webhooks (
list_configured_webhooks) and generate signatures (generate_webhook_signature), allowing your agent to integrate transcription results into other systems automatically. - Optimizes accuracy on demand. Before transcribing, run
list_supported_languagesor check available models vialist_transcription_modelsto ensure the best engine is running for your specific content.
Real-World Use Cases
Processing a backlog of client interviews.
A marketing director has 30 audio files and needs transcripts. Instead of manually listing them, they tell their agent: 'Transcribe these 30 URLs.' The agent runs transcribe_audio_url for all links. It then waits, periodically calling get_transcription_status, until the job is finished. Finally, it calls get_transcription_export to get them all in a single ZIP file.
Debugging an account billing issue.
The ops engineer suspects over-usage. They ask their agent: 'What did we process last month?' The agent runs get_usage_statistics and cross-references the total volume with what was expected, while also calling list_transcription_history to pinpoint which jobs were largest.
Building a robust internal reporting system.
A development team needs transcription results to automatically update Jira tickets. They use the agent to run transcribe_audio_url. The server then triggers a webhook, and the agent uses generate_webhook_signature to ensure that incoming data payload is legitimate before processing it.
Cleaning up old jobs and checking language needs.
A researcher finished a project and needs to clear old records. They first run list_transcription_history to see the IDs, then use remove_transcription_job on the unnecessary records. Before starting new work, they confirm locale support by running list_supported_languages.
The Tradeoffs
Assuming job completion.
Just calling the transcription URL and assuming the text is instantly available in the chat. This fails because most jobs are asynchronous.
→
You must check status first. Run transcribe_audio_url to start, then repeatedly use get_transcription_status until the job reports 'Complete'. Only then call get_transcription_export.
Ignoring billing limits.
Running a massive batch of transcriptions without checking credits, resulting in an API error halfway through and unexpected downtime.
→
Always run get_remaining_credits at the start of any major workflow. This prevents costly failures mid-job.
Using a generic text tool.
Copying audio to a general purpose AI agent and asking it to 'transcribe this file.' The output is often formatted poorly or lacks metadata.
→
Use the dedicated transcribe_audio_url tool. It guarantees high accuracy, and you can follow up with list_transcription_history for structured metadata.
When It Fits, When It Doesn't
Use this MCP Server if your core workflow involves converting audio files into usable text assets—think podcasts, interviews, or meeting recordings. The server handles the entire lifecycle: starting the job (transcribe_audio_url), monitoring its progress (get_transcription_status), and pulling the final export (get_transcription_export).
Don't use this if you only need to transcode audio (you need a dedicated media library tool) or if your primary goal is merely sending messages. If you are building an internal system, check generate_webhook_signature and list_configured_webhooks first; those tools show if the server supports advanced integration beyond simple transcription.
If you just want to list files without processing them, this isn't it. You need audio input to use any of its core functions.
Common Questions About Speechnotes MCP
How do I know if my API key is set up correctly with Speechnotes MCP Server? +
You run the test_speechnotes_auth tool. This confirms your credentials work before you waste time running a full transcription job.
What if I need to transcribe an audio file in French? Which tool do I use? +
First, run list_supported_languages to get the correct code. Then, use that language setting when you call transcribe_audio_url.
How can I check my account limits using Speechnotes MCP Server? +
Use get_remaining_credits. This tells you exactly how many transcription jobs or credits you have left before starting any large process.
How do I check the real-time progress of a running job using the `get_transcription_status` tool? +
The get_transcription_status tool provides immediate updates on your audio file. It tells you whether the transcription is still processing, if there were errors, or when it's ready to export.
When I use `get_transcription_export`, what are the available document formats for my text? +
The tool lists supported output file types. You can request results in multiple common formats, including plain TXT, DOCX, and SRT files.
How do I review detailed metadata for all past jobs using `list_transcription_history`? +
Running list_transcription_history retrieves a full list of your completed transcriptions. This data includes timestamps and the total number of speakers identified for each job.
How can I manage or verify my webhook endpoints using `list_configured_webhooks`? +
This tool lists all the delivery addresses connected to your account. It lets you confirm which external systems will receive automatic notifications when a transcription job finishes.
If I need to clear out an old or failed record, how do I delete it using `remove_transcription_job`? +
You use the remove_transcription_job tool and provide the specific Job ID. This action deletes the job's record entirely from your history.
How do I find my Speechnotes API credentials? +
Log in to your Speechnotes account and navigate to the API or developer section in your dashboard to find your unique API Key and API Secret.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.