talkthrough-mcp
Converts unstructured video/audio recordings into searchable, agent-ready structured data with speaker identification and visual content extraction, reducing…
Recordings in. Agent-ready evidence out. Local-first MCP for transcripts, keyframes, OCR, speakers, and wall-clock evidence.
- Generate transcript text from video recordings with speaker identification and timestamps.
- Extract keyframes and OCR text from video files for documentation and evidence review.
- Automate creation of timestamped evidence reports from recorded meetings and demonstrations.
Converts unstructured video/audio recordings into searchable, agent-ready structured data with speaker identification and visual content extraction, reducing manual review time for compliance, incident analysis, and knowledge capture workflows.
Compliance teams and incident investigators processing recorded meetings, calls, or system demos requiring timestamped evidence with speaker attribution.
https://github.com/korovin-aa97/talkthrough-mcp
By korovin-aa97
How to Get It
claude mcp add talkthrough-mcp -- npx -y talkthrough-mcp
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Help me generate transcript text from video recordings with speaker identification and timestamps
Trust Signals Auto-scanned
Data & Access
Community Pulse Emerging
Discussed on Hacker News
- Show HN: Turn narrated screen recordings into data for AI agents (local, MIT) — Hacker News · 1 pts
1 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 30 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 30 GitHub stars; contributors unknown; last commit 0d ago; license MIT.