speech-to-textmultimediaocrsearchincident-analysis
Indexes local video and audio into searchable transcripts, keyframes, OCR results, and wall-clock evidence for agent workflows.
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Overview
Use cases
Analyze narrated bug recordings
Search meeting and workshop recordings
Correlate spoken incidents with logs
Extract OCR and frames from product demos
Key features
Local Whisper transcription
Scene keyframe extraction
OCR for recorded screens
Full-text evidence search
Wall-clock timestamp anchoring
Public video URL processing
Capabilities & Tool Schemas
Inspect callable tools, capabilities, and parameters exposed to AI agents by Talkthrough MCP.
Extracted Tool Capabilities
Local Whisper transcription
Scene keyframe extraction
OCR for recorded screens
Full-text evidence search
Wall-clock timestamp anchoring
Public video URL processing
How Talkthrough MCP works
What korovin-aa97/talkthrough-mcp MCP server does
The korovin-aa97/talkthrough-mcp MCP server converts video and audio into structured, agent-ready evidence. Its processing pipeline can produce a local Whisper transcript, scene keyframes, OCR text, full-text search data, and wall-clock anchoring. Wall-clock anchoring connects remarks in the recording to real timestamps, which supports comparing spoken events with logs or other time-based records.
Inputs can be local .mov and .mp4 files, other local video or audio formats, and public video links when the URL extra is installed. The project describes workflows for narrated bug recordings, meetings, workshops, product demonstrations, and production incidents. It does not post issues or send processed media to a tracker; tracker actions remain the responsibility of the surrounding agent workflow and its own tools.
How it works
The korovin-aa97/talkthrough-mcp MCP server runs as a local stdio MCP process. An agent sends media-processing and evidence-search requests through MCP, while the server performs transcription, frame extraction, OCR, and indexing on the local machine. The resulting evidence can then be searched or used to assemble material such as an issue draft.
Whisper models download on first use, and media or model assets may require network access during initial setup. Subsequent jobs can reuse shared Whisper, OCR, and diarization caches. The README also notes that a bundled FFmpeg build can be fetched when a system FFmpeg installation is unavailable.
Setup and configuration
The documented runner is uvx, with Python constrained to versions 3.11 through 3.13. A direct setup command is:
The diarization extra enables the installation variant shown in the README, while url supports public video-link workflows. The only stated prerequisite for the quickstart is uv; the project says FFmpeg falls back to a bundled build and that OCR is installed through Python packages.
The server can be registered in Claude Desktop, Cursor, Cline, Windsurf, and other MCP clients using a stdio configuration. Claude Code also supports adding the server with claude mcp add, or installing the repository’s full plugin for its additional slash commands, triage subagent, and skill. The plugin and the standalone server should not both be registered in the same Claude Code setup.
Tools and capabilities
The README describes nine MCP tools and six prompts, although the provided material does not enumerate their individual names. The supported capability areas are:
- Process local video and audio recordings.
- Accept public video links with the URL-enabled installation.
- Generate local Whisper transcript segments.
- Extract scene keyframes and exact frames for visual evidence.
- Run OCR over recorded screen content.
- Search indexed evidence using full text.
- Map spoken remarks to wall-clock timestamps.
- Support diarization through the documented installation extra.
These outputs are intended for agent investigations and evidence-backed drafts rather than for automatically filing issues. The example workflow uses the resulting evidence with separate tracker tooling.
Limitations and notes
The korovin-aa97/talkthrough-mcp MCP server is local-first: the README states that it does not use a cloud service or an LLM inside the server. It also does not leave the machine to post issues. Agents still need separate tools if they must create tickets or interact with a source-control or issue-tracking system.
First-run processing can be slow because uvx resolves the environment and process_media may download media, Whisper, OCR, or diarization assets. Updating the plugin can create a new environment and may cause the bundled FFmpeg download to happen again. Silent recordings can produce keyframes and OCR while yielding no transcript segments because there is no audio track.
The project targets Python 3.11–3.13 and is released under the MIT license. No API key or cloud credential is described in the supplied material.
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CategoryMultimedia Process
PricingFree
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Scanned 20d ago via OSV.dev · --python (PyPI)
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