AssemblyAI MCP. Turn any audio or video URL into structured data.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
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AssemblyAI. Transcribe audio and video files with industry-leading accuracy. This server detects speakers, pulls automated summaries, and extracts insights like sentiment and key topics directly from spoken content.
It handles everything from simple transcription to full content discovery, all through natural conversation with your agent.
What your AI agents can do
Delete transcript
Removes a specific transcript record from your history.
Get chapters
Retrieves automated chapter markers and time codes for the transcript.
Get sentiments
Analyzes the transcript to determine the overall emotional tone, noting shifts in sentiment.
Sends an audio or video URL to transcribe the content, making the resulting transcript available to the agent.
Checks the progress or retrieves the final result of a previously submitted transcription job.
Generates a concise summary of the full transcript content using the get_summary tool.
Runs the transcript through sentiment analysis to pinpoint emotional tones and emotional shifts.
Separates the transcript into distinct segments and assigns a speaker label to each utterance.
Scans the content to identify and list the main subjects or themes discussed in the audio.
Retrieves automatically generated chapters and time markers, structuring the content for easy navigation.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
019dd0bddelete transcript
Removes a specific transcript record from your history.
019dd0bdget chapters
Retrieves automated chapter markers and time codes for the transcript.
019dd0bdget sentiments
Analyzes the transcript to determine the overall emotional tone, noting shifts in sentiment.
019dd0bdget speakers
Identifies and labels every unique speaker within the transcript.
019dd0bdget summary
Generates a concise, high-fidelity summary of the entire transcribed content.
019dd0bdget topics
Scans the transcript and returns a list of the main subjects or topics discussed.
019dd0bdget transcript
Checks the current status of a transcription job or retrieves the final transcript text.
019dd0bdlist transcripts
Shows a list of your most recent completed or pending transcription jobs.
019dd0bdtranscribe audio url
Sends a public URL to start the transcription job for an audio or video file.
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 AssemblyAI, 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're hooking up your AI agent to AssemblyAI, so you can nail high-fidelity audio intelligence without ever uploading files to some sketchy web portal. Your agent handles all the heavy lifting. You send a public URL to transcribe_audio_url to kick off the transcription job for an audio or video file, and the resulting transcript is ready for your agent to work with.
When you're done with the basic transcription, your agent can do way more. You can call get_transcript to check the job's status or grab the final transcript text. You'll also see a list of your recent jobs with list_transcripts.
For deep analysis, you can pull out automated summaries using get_summary. You can run sentiment analysis with get_sentiments to pinpoint the emotional tones and even track where the mood shifts. To separate who said what, you use get_speakers to identify and label every unique speaker in the transcript. You can scan the content for the main subjects discussed by calling get_topics.
You'll also get automatically generated chapters and time markers for easy navigation by running get_chapters. If you need to wipe a transcript record, you can delete it from your history using delete_transcript.
How AssemblyAI MCP Works
- 1 First, you call
transcribe_audio_urland provide the URL of the audio or video you need transcribed. - 2 Next, you use
get_transcriptto monitor the job status. Once the job is complete, you call the intelligence tools (e.g.,get_summary,get_sentiments) with the job ID. - 3 Finally, you get the structured data—like a summary, list of topics, or speaker-labeled transcript—and use that output in your conversation.
The bottom line is, your AI client handles the whole pipeline: transcribes the media, waits for it to finish, and then runs all the analysis tools in sequence.
Who Is AssemblyAI MCP For?
Content creators and corporate teams who generate massive amounts of spoken content. This is for anyone tired of manually transcribing podcasts, summarizing meeting recordings, or piecing together who said what in a long webinar. It hands you structured, actionable data without leaving your chat window.
Generates instant podcast transcripts and video chapters by sending the raw URL to the agent.
Summarizes customer service calls and analyzes sentiment across call recordings to spot recurring issues.
Integrates high-speed speech-to-text intelligence into custom workflows by simply querying the agent.
What Changes When You Connect
- See the full context of a meeting by calling
get_speakers. This separates every utterance by speaker label, so you know exactly who said what. - Get immediate insight into customer calls by running
get_sentiments. The agent pinpoints emotional shifts, letting you know if a conversation started positive but ended frustrated. - Stop manually reading transcripts. Call
get_summaryto get a high-fidelity, automated summary of the entire audio file right away. - Keep track of all media assets with
list_transcripts. You can see your 5 most recent jobs and check if they're ready to analyze. - Structure your media library with
get_chapters. This tool pulls automated chapter markers and time codes, making massive videos navigable. - Determine the core focus of a long presentation by running
get_topics. It lists the main subjects discussed, so you don't have to read the whole thing.
Real-World Use Cases
Post-Podcast Analysis
A content creator uploads a podcast URL. The agent first runs transcribe_audio_url. Once complete, it calls get_chapters and get_summary. The creator gets a time-stamped outline and a one-paragraph recap, ready for their show notes.
Customer Support Triage
The support manager needs to know if a call was successful. They send the call URL, and the agent runs get_sentiments. The result shows the overall sentiment and flags specific moments where the tone dipped, helping the team prioritize follow-up.
Meeting Minutes Drafting
During a client review, the team records the discussion. The agent runs transcribe_audio_url and then calls get_speakers. The resulting output is a perfectly formatted, speaker-attributed draft of the meeting minutes.
Research Data Synthesis
A researcher has 20 hours of interview audio. The agent runs transcribe_audio_url and then calls get_topics. This gives the researcher a structured list of the 5 main subjects covered, allowing them to focus their analysis.
The Tradeoffs
Treating transcripts like text files
Copying a raw transcript and pasting it into a generic summarizer that treats all words equally. You lose the context of who said what, when they said it, or if the mood shifted.
→
Use the agent to first run transcribe_audio_url, then call get_speakers and get_sentiments. This preserves the attribution and the emotional context, making the summary accurate to the conversation.
Manually tracking job status
Having to switch between a web portal, check the job ID, wait 10 minutes, and then come back to the chat window to see if the transcription is done. It’s slow and fragile.
→
Let the agent handle the process. You start with transcribe_audio_url, then ask the agent to get_transcript status. The agent handles the waiting and notifies you when the data is ready for analysis.
Over-relying on single-pass tools
Just running get_summary on the raw transcript. You get a summary, but you miss the key topics or who was responsible for the decision mentioned in the audio.
→
Chain the tools. After transcription, run get_topics to define the scope, then run get_summary for the overview, and finally use get_speakers to attribute ownership to the key points.
When It Fits, When It Doesn't
Use this if your goal is to turn raw, unstructured audio or video into verifiable, structured data points. You need to know who said something, when it happened, and how the conversation felt. It's ideal for compliance, research, or content production workflows.
Don't use it if you just need simple text translation or keyword search. If you only need a basic word count, a simple text API is fine. If you only need a single piece of data (like just a list of names), consider if a simpler tool is available, but for complex analysis, this server is necessary. It handles the whole pipeline, from raw media ingestion to multiple layers of analysis.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AssemblyAI. 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 9 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Writing up meeting minutes shouldn't require 15 minutes of copy-pasting.
Today, you record a meeting, then you spend hours transcribing the raw audio. You copy the text into a document. You then manually read through it to figure out who said what, and you often lose the emotional context or the actual action items buried deep in the discussion.
With this MCP server, you send the meeting URL to your agent. The agent handles the transcription, runs `get_speakers` for attribution, and then runs `get_summary` to give you the core takeaways. You get a clean, actionable document, instantly.
AssemblyAI MCP Server: Get actionable data from audio.
You used to have to upload audio to a separate web portal, wait for a human transcriber, and then download the file. If you needed sentiment analysis, you had to re-upload it or use a different service entirely. It was a messy, multi-step process.
Now, you just talk to your agent. You provide the URL, and the agent runs the entire workflow—transcription, speaker labeling, and analysis—and hands you the structured data right back into the chat.
Common Questions About AssemblyAI MCP
How do I use the `transcribe_audio_url` tool? +
You give the agent a public URL for the audio or video. The agent initiates the transcription job. It won't give you the final text immediately; you must then use get_transcript to check the status and retrieve the result when it's ready.
What is the difference between `get_summary` and `get_topics`? +
get_summary gives you a paragraph-long overview of the content. get_topics lists the core subjects discussed in the audio, which is better if you want to know the high-level themes, not just a general overview.
Can I tell who spoke what using `get_speakers`? +
Yes. get_speakers retrieves detailed utterances and labels them with the specific speaker ID, ensuring you know exactly which person spoke each line.
Do I need to use `list_transcripts` before running analysis? +
No. You use list_transcripts only if you need to see your past jobs. For a new analysis, you start by running transcribe_audio_url.
Is the analysis done on the uploaded file or a URL? +
This server processes public URLs. You give the agent a direct link to the audio or video file, and the analysis happens on that hosted content.
How do I check the status of a transcription job using `get_transcript`? +
You use get_transcript to check the job status. If the job is still processing, the tool returns a status code and an estimated completion time. Once finished, you can proceed to run analysis tools.
Do I need to list transcripts with `list_transcripts` before using other tools? +
No, you don't need to list them first. However, list_transcripts helps you find the correct job ID or name for the specific transcript you want to analyze with tools like get_summary or get_sentiments.
What happens if I try to delete a transcript using `delete_transcript`? +
The delete_transcript tool permanently removes the transcript data associated with the given ID. It's best to run this only after confirming you no longer need the record.
How do I find my AssemblyAI API Key? +
Log in to your account, navigate to the API Keys section in your dashboard, and copy your personal token.
Can I automatically summarize audio via AI? +
Yes! Enable the summarization option when transcribing, then use the get_summary tool to retrieve the high-fidelity AI synopsis.
How do I check transcription status? +
Use the get_transcript tool with the job ID provided at submission to monitor the status (Queued, Processing, Completed) in real-time.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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