The Supercut MCP gives your AI/coding assistants permission-aware access to recordings, including semantic search, transcripts, frames, comments, reactions, and more.
Really excited to share Supercut Agents with you. 🚀
We’ve exposed Supercut through MCP so compatible assistants can access transcripts, frames, comments, reactions, and semantic search over recordings, including content shared with you, not just your own. The goal is to make video context queryable, permission-aware, and directly usable inside agent workflows.
The amount of use cases this opens up is bananas...
- After recording a new feature walkthrough, have an agent draft an Intercom help article from the transcript and video frames.
- Weekly sweep of sales calls for objections, competitor mentions, and buying signals, then update the CRM.
- After each client review, extract requested changes and create tasks in Linear or Jira.
- Every morning, scan the previous day’s team updates for action items.
- When a decision is mentioned in an async update, capture it and attach it to the relevant project note.
- On a recurring schedule, search across shared recordings for a topic like pricing, churn, or integration feedback, then route the results into the right tool.
Would love to hear your use cases!
MCP on top of video context is the right move, most agent workflows are text-only because video has always been a black box. The permission-aware semantic search is the part I'd want to stress test. When an agent queries "pricing objections across all sales calls", how does it handle recordings where the sharer has partial view permissions?
Congrats on the launch, David. The MCP angle is interesting because it turns recordings into something agents can actually use, not just files people forget to watch later.
I’m curious, when teams start using Supercut this way, is the bigger value helping agents find the right moment in a recording, or helping the team trust that the extracted context is strong enough to turn into a doc, ticket, CRM update, or next action?
Semantic search over team recordings is the MCP resource I've been waiting for. Curious how granular the permission layer is - does the agent inherit per-recording share settings, or is it a flat workspace-level toggle?
I understand this is for teams that have a large video archive and record everything, right? For example, in our case videos are rarely recorded, and for notes we use a regular AI that listens and then produces a meeting report, which is sent by email to everyone who attended the meeting.
Building permission awareness directly into the agent access layer rather than bolting it on top is exactly the right architectural call. At RetainSure we work with customer call recordings for CS insights, and the consent and access layer is always where things get complicated. How does the permission model work at query time? Is access enforced at the metadata layer or does it delegate to the underlying recording storage?
Exposing semantic search over frames and transcripts through an MCP interface is clever. The permission model giving agents structured access without raw video is cleaner than anything I've seen. We've lost too much engineering context in unindexed Loom links. How does the semantic search handle multi-speaker transcripts? Do you embed at ingest time, and how do you chunk long recordings for retrieval?
About Supercut for Agents on Product Hunt
“Permission-aware AI access to recordings and metadata”
Supercut for Agents launched on Product Hunt on May 20th, 2026 and earned 148 upvotes and 15 comments, placing #7 on the daily leaderboard. The Supercut MCP gives your AI/coding assistants permission-aware access to recordings, including semantic search, transcripts, frames, comments, reactions, and more.
Supercut for Agents was featured in Productivity (652.1k followers) and Developer Tools (512.7k followers) on Product Hunt. Together, these topics include over 203.2k products, making this a competitive space to launch in.
Who hunted Supercut for Agents?
Supercut for Agents was hunted by David OKuniev. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Supercut for Agents stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
David OKuniev