Supercharge your AI with Symbl.ai. Turn raw media into structured, actionable intelligence.
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Symbl.ai extracts structured intelligence from raw audio, video, and text transcripts. Your agent passes media files—whether recorded calls or chat logs—to this server.
It returns clean data: summaries of key topics, a list of assigned action items, specific follow-up tasks, and custom keyword reports for compliance checking.
What your AI can do
Create tracker
Sets up a monitor to count occurrences of specified keywords or phrases in conversation data.
Get action items
Extracts all tasks and follow-up items from an already processed conversation transcript.
Get follow ups
Retrieves specific actions that need to be taken after a discussion or meeting.
Pass audio, video, or text to generate a Conversation ID required for all subsequent analysis.
Creates a concise digest of the entire conversation, pulling out the main ideas and scope.
Automatically extracts specific tasks mentioned in the transcript, often including who is responsible.
Analyzes the content and returns a list of primary themes discussed during the call or meeting.
Creates custom trackers to count how often specific words, phrases, or compliance terms appeared in the data.
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Compatible AI Apps
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Symbl.ai MCP Server: 9 Tools for Conversation Analysis
Process media inputs (audio, video, text) to extract specific intelligence, like action items or key topics, using a guided workflow.
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 Symbl.ai on VinkiusCreate Tracker
Sets up a monitor to count occurrences of specified keywords or phrases in conversation data.
Get Action Items
Extracts all tasks and follow-up items from an already processed conversation...
Get Follow Ups
Retrieves specific actions that need to be taken after a discussion or meeting.
Get Questions
Identifies all unanswered questions raised during the conversation for later review.
Get Summary
Generates a high-level summary that captures the core discussion and outcomes of a...
Get Topics
Identifies and lists the main themes and topics covered across an entire chat or meeting.
Process Audio
Processes an audio file (base64) to extract insights and returns a unique Conversation ID.
Process Text
Processes text conversations by returning a unique Conversation ID for subsequent...
Process Video
Processes a video file (base64) to extract insights and returns a unique...
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token from
cloud.vinkius.com. For OAuth-protected servers, expand Advanced
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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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 Symbl.ai. 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 connection provides 9 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Reviewing meeting notes should not require three different tabs and two hours of copy-pasting.
Right now, reviewing a week's worth of calls means opening transcripts in one tab, pulling action items into a spreadsheet on another, manually summarizing key decisions in a third document, and then trying to map all the identified topics across four different files. It’s tedious copy-pasting just to create a single source of truth.
With Symbl.ai MCP Server, you process the media once—audio, video, or text—and get that central Conversation ID. You then call `get_summary` and `get_action_items` in sequence. You get clean, structured JSON data ready to populate a dashboard. The whole manual review step just disappears.
Get Summary and Action Items from Media with Symbl.ai MCP Server
Previously, finding follow-ups meant scrubbing through the full transcript looking for phrases like 'we need to...' or 'I'll get back to you on...'. You missed things because they were buried in jargon or tangents.
Now, run `get_action_items` against your processed media. The tool pulls out the assignments directly—who needs to do what and by when. It’s a precise extraction layer that turns noise into clear ownership.
What your AI can actually do with this
You pass raw media—whether it's a meeting recording, a chat log, or a video call—to this server. It’ll spit out clean, structured data you can actually act on.
To start the whole thing off and get your Conversation ID, your agent first hits one of three entry points: process_audio takes a base64 audio file and spits back a unique identifier; process_video does the same thing for video files; or if you’ve got text transcripts, you use process_text to generate that initial Conversation ID.
Once you've got that ID, you can run deep analysis. You can call get_summary, and it generates a high-level digest of everything discussed—the core outcomes and the main scope of the conversation, keeping you from having to read through hours of garbage footage. To figure out what actually needs doing, you use get_action_items; this tool automatically pulls out every single task mentioned in the transcript, usually noting who owns it.
If you just need a general feel for follow-up work, you hit get_follow_ups, and it retrieves those specific action items that gotta get done after the meeting wrap-up.
To keep track of what was discussed without manually reading transcripts, you run get_topics. This analyzes the content and gives you a list of primary themes—the major topics covered across the entire call or chat. If anything got left hanging in the air during the discussion, running get_questions identifies all those unanswered questions so you can circle back later.
For compliance checks or market research, tracking specific words is key. You use create_tracker to set up a monitor that counts how often certain keywords or phrases pop up throughout the whole conversation data. This mechanism lets you track everything from client sentiment shifts to mentions of required regulatory terms.
The server’s intelligence doesn't stop there; it keeps pulling out every piece of structured insight, making sure you don't miss anything important.
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Here's how it actually works
The bottom line is: you feed it raw media, get an ID, and then ask for exactly what you need from that conversation.
First, pass your raw media (audio, video, or text) to a processing tool (process_video, process_audio, etc.). This returns a unique Conversation ID.
Next, use that Conversation ID with a specific getter tool (e.g., get_summary or get_action_items) to pull out the desired structured data point.
The client receives the clean, formatted output—like a list of action items or key topics—ready for immediate use.
Who is this actually for?
Product Managers who sit on end-user interviews; Sales reps drowning in call transcripts; or Developers building complex data pipelines. If your job involves reading hundreds of unstructured communications a week, this saves you hours.
Feeds raw user interview audio into the server, then uses get_questions and get_topics to quickly distill key pain points and feature gaps.
Pumps call transcripts through process_text, followed by get_follow_ups and get_action_items to ensure no potential deal point or follow-up task gets missed.
Runs large batches of communication logs using create_tracker to check for mandatory disclaimers or specific regulated phrases across all recorded interactions.
What Changes When You Connect
Stop reading through hours of video to find one key decision. Use get_summary on a processed file—you instantly get the core outcomes and next steps.
Never lose track of who needs to do what again. Run get_action_items against any transcript to build an immediate, assignable task list.
Identify market trends across dozens of interviews without manual coding. Use get_topics to cluster all the main themes discussed in your research pool.
Maintain compliance by monitoring specific terms. Set up a create_tracker with sensitive keywords; it counts every instance automatically.
Handles diverse inputs, from recordings to chats. You process media using process_audio, process_video, or process_text—it doesn't care what format the data is in.
See it in action
Analyzing a QBR (Quarterly Business Review)
A sales director receives 30 hours of recorded calls. Instead of manually reviewing them, they use process_audio for each file, then run get_topics on the resulting IDs to build an immediate map of recurring customer needs and competitive mentions.
Distilling User Feedback from Interviews
A PM collects 20 user interviews. They process all audio files via process_audio. Then, they chain the results: run get_questions to identify feature gaps and use get_action_items to assign the follow-up interview tasks.
Reviewing Internal Compliance Logs
A compliance officer needs to audit all calls for mentions of a specific regulation. They feed transcripts into process_text, then immediately use create_tracker to count every instance of the required legal phrase.
Following Up After a Strategy Meeting
A project lead uploads the meeting video via process_video. They run two tools: get_follow_ups and get_action_items to ensure they get both the high-level next steps and the detailed, assigned tasks.
The honest tradeoffs
Summarizing raw video bytes
Just pasting a giant chunk of base64 data into get_summary thinking it will work. The tool needs context and an ID first.
You must first run the media through its specific processor (process_video, for example). This generates the Conversation ID, which you then pass to get_summary. It's a two-step process.
Looking only at topics
Using get_topics and assuming it covers everything. Topics tell you what was discussed; they don't assign responsibility.
Always follow up topic discovery with get_action_items. The topics define the scope, but the action items tell you who needs to move things forward.
Ignoring text vs. media
Assuming that just because a chat log is text, it's as good as an audio recording for insights.
Use process_text when you have written logs; use process_audio or process_video when the context comes from spoken word. The source dictates which processor to run.
When It Fits, When It Doesn't
Use this server if your core need is transforming unstructured communication (transcripts, recordings) into structured data points like lists of tasks, identified topics, or summaries. You're doing content extraction, not just messaging. Don't use it if you simply want to send a reminder message or check the status of a ticket—that's for standard ticketing systems. If you only need simple text analysis (like sentiment scoring), a generic NLP service might suffice. But when you need to analyze spoken intent across multiple media types, this is what you use. The key pattern is: Process Media -> Get ID -> Query Data.
Questions you might have
How do I use `process_audio` with Symbl.ai? +
You send the audio file (base64) to process_audio. This returns a unique Conversation ID, which you must save. You cannot query any other tool without this specific ID.
Is `get_summary` better than just reading the transcript? +
Yes. Reading the raw text is inefficient because it's linear. get_summary condenses thousands of words into a few key bullet points, focusing only on outcomes and major discussion shifts.
What’s the difference between `get_follow_ups` and `get_action_items`? +
While similar, get_action_items focuses on concrete tasks assigned to a person. get_follow_ups tends to pull out broader steps or commitments that need monitoring.
Can I track keywords across different media types using Symbl.ai? +
Yes, you process the media first (e.g., via process_video) and then use create_tracker on the resulting Conversation ID to monitor specific phrases like compliance terms.
What credentials do I need to pass when calling `process_text`? +
You must supply your Symbl.ai App ID and the corresponding App Secret Key. These two values authenticate your agent, proving you have permission to run the analysis.
Do I need to call a processing tool before running `get_summary` or `get_action_items`? +
Yes. The follow-up tools require a Conversation ID first. You must use one of the initial process functions (like process_audio) to generate this unique ID.
How does Symbl.ai handle large files when I call `process_video`? +
Video and audio inputs need to be encoded as a base64 payload for the API call. This standard encoding ensures the agent correctly transmits the raw binary media data.
If my request fails while running `create_tracker`, what should I check? +
Check the returned error message for specific failure codes, especially rate limiting signals. If limited, implement an exponential backoff delay before retrying your tool call.
How do I get a summary of a processed conversation? +
Use the get_summary tool by providing the specific Conversation ID. The agent will return a structured summary of the key points discussed.
Can I detect specific keywords in my audio or text files? +
Yes! Use the create_tracker tool to define a list of keywords or phrases. Symbl.ai will then monitor and detect these within your processed conversations.
What insights can I extract from a conversation ID? +
You can use get_topics, get_action_items, get_follow_ups, and get_questions to retrieve specific intelligence layers from any previously processed media.
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