A
licenseNot graded
qualityD
maintenanceEnables AI coding agents to capture screen and voice recordings, extract timestamped frames, and receive structured Markdown reports with context for bug fixing and UI feedback.
15 npm
18
MIT
talkthrough-mcp
by korovin-aa97
Related Servers
Alternatives to talkthrough-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceLocal-first bug reports for coding agents: annotated screenshots, recordings and notes stored on your machine, served over MCP with the images included. Sync reports to GitHub issues when you choose.7MIT
Jot MCP Serverofficial
AlicenseAqualityCmaintenanceEnables AI coding agents to inspect user feedback on live web apps, read full reports and recording timelines, update status, and reply in threads so fixes can be checked and confirmed by reporters.5MIT- AlicenseAqualityCmaintenanceTurn screen recordings into automated Playwright E2E reproduction tests, structured GitHub bug reports with clickable video timestamps, and SOP walkthroughs.621 npmMIT
- AlicenseNot gradedqualityBmaintenanceAn MCP server that watches for screen recordings, transcribes spoken feedback locally, and attaches each comment to the relevant frame, enabling coding agents to see and fix the issues you mention.6MIT
- AlicenseAqualityBmaintenanceVisual feedback as agent work packets: stakeholders pin on your live app and your AI coding agent reads each pin (selector, screenshot, DOM, thread, acceptance criteria) via MCP and ships the fix.39899 npm1MIT
TDQS
A4.9/5.0
Scored across 9 tools
Disambiguation5/5
Each tool targets a distinct stage of the pipeline: ingestion (process_url vs process_media), browsing (list_jobs), retrieval (get_transcript, get_frames, extract_frame, get_moment), search, and annotation (label_speakers). The descriptions include explicit 'When NOT to use' sections and anti-examples that sharply delineate boundaries, e.g. get_frames (stored keyframes) vs extract_frame (exact native-resolution frame) vs get_moment (bundled evidence).
Naming Consistency5/5
Names are uniformly snake_case and follow a predictable verb_noun pattern (process_url, process_media, list_jobs, extract_frame, get_frames, get_moment, get_transcript, label_speakers). The single tool 'search' is a minor stylistic deviation but remains natural and readable within the set.
Tool Count5/5
Nine tools is well-scoped for a media ingestion and analysis server. Each tool earns its place by covering a required capability without redundancy or bloat.
Completeness4/5
The surface covers ingestion (local and URL), listing, transcript retrieval, frame access, evidence bundling, lexical search, and speaker labeling—a complete workflow. The one notable gap is a delete/GC tool for managing stored jobs; the server only mentions a CLI `talkthrough-mcp gc`, leaving no in-band way to remove recordings.
Maintenance
ActivityMaintained
ResponsivenessLimited data