Analyzing narrated recordings with talkthrough

The talkthrough MCP server turns a local video/audio file into queryable structured data: timestamped transcript segments, scene keyframes, OCR'd on-screen text, and wall-clock anchoring. No LLM inside — you bring the reasoning; it brings the evidence. Everything is lazy and token-budgeted: never ask for more than the moment you are analyzing.

Prerequisite

The talkthrough MCP server must be connected (tools like process_media / get_transcript are visible). If not, tell the user to install it: claude mcp add -s user talkthrough -- uvx --python ">=3.11,<3.14" "talkthrough-mcp[diarization,url]" (see the repository README for other clients).

Core workflow

1. Ingest once: process_media(path) — idempotent by content hash; re-calls on the same file return instantly. Given a public video/audio URL instead of a file, call process_url(url): the source is downloaded once (the only network step; YouTube needs the [url] extra) and kept inside the job, then everything below is identical and local — never download twice; a repeat call serves the stored job. Long videos take minutes and stream progress. The summary gives you job_id, counts, wall-clock, and a transcript preview — do NOT dump anything else eagerly. Multi-person recording (meeting/interview)? Add diarize=true — even when the ask is just "summarize", speaker structure is part of meeting analysis — and — whenever the headcount is known — num_speakers=N (the main accuracy lever): segments get S1/S2/… labels and the summary a talk-time roster. On an already-processed job the amend re-runs ONLY diarization (no re-transcription) — still minutes on long recordings.
2. Orient: get_transcript(job_id) (paginate via next_start_ms when truncated) or search(job_id, "<distinctive word>") to jump straight to the relevant moments (searches speech AND on-screen OCR text). Multi-word search defaults to match_mode="all_words"; use "any_word" for broader lexical recall.
3. Evidence per remark: get_moment(job_id, t0-2000, t1+2000) — one call returns the transcript slice + up to 3 unique frames + their OCR text + the wall-clock range. This is the workhorse; describe observed from the returned pixels, never from imagination.
4. Precision when needed: get_frames(at_ms=...) for nearby keyframes; extract_frame(job_id, at_ms, crop={x,y,w,h}) for an exact instant at native resolution (keyframes capture scene changes + a 1 fps floor, so sub-second moments can fall between them).
5. Keep verified names: after proving an anonymous label's identity, call label_speakers(job_id, labels={"S1":"Name"}, evidence={"S1":"intro or frame proof"}). Saved names appear in later transcript, moment, and search calls while raw S1/S2 labels remain. If a diarization amend changes labels, those names move to speaker_names_pending_review and stop being identities. Use the stored old-roster context anchors to re-check them. A pending label still in the roster can be confirmed, replaced, or removed; a stale pending label can only be removed with labels={"Sx":null}. Never use a pending name in minutes or search as though it were active. A full force=true rebuild of a job with active or pending names must also use diarize=true; it rebuilds safely and moves every old identity to pending review, while omitting diarization is refused without changing the stored job.
6. Recall across sessions: list_jobs() — the store persists; a file processed yesterday (even via CLI) is queryable by job_id today.

Timestamps

Every timestamped result carries t_ms (video-relative) and, when the recording start is known, t_wall (ISO 8601 real time). Copy t_wall VERBATIM from the payload — never compute it from t_ms yourself (hand-derived wall-clocks drift by whole hours). Use t_wall to correlate remarks with server/app logs (±30 s grep window). If wall_clock is null or low-confidence, ask the user when the recording started and re-anchor: process_media(path, recorded_at="<ISO 8601>", force=true); when the job already has speaker identities, include diarize=true as required by the safe-rebuild contract.

Packaged workflows (server prompts)

Prefer the server prompts when the task matches — they encode the full method: bug (one recording → evidence-backed GitHub issue draft; silent, narration-free recordings welcome), triage-recording (screencast → findings JSON per the contract in examples/output-contract.schema.json), spec-from-workshop, backlog-from-demo, meeting-actions (audio-only friendly), correlate-with-logs.

Rules of thumb

• Audio-only jobs (.m4a/.mp3/…): transcript tools work; frame tools error by design — that error is expected, not a failure.
• Speaker labels are anonymous (S1/S2, ordered by first voice). Mapping them to names is YOUR job: self-introductions, vocatives, the attendees list — and on video jobs the screen check is MANDATORY: for every label you map, get_frames(at_ms=<that label's longest_turn_at_ms from the roster>) and read the meeting-app name plates, the recording's title card, the active-speaker highlight BEFORE asserting the mapping. STT homophones lie about name spellings (spoken "profit" vs on-screen "Prophet") — trust OCR/frames over the transcript for names. State the mapping explicitly and mark unmapped labels "unidentified". Roster name_candidates are raw OCR hints, not identities: they may be UI text, a job title, or somebody else's name. Inspect the cited frame and persist only defensible mappings with label_speakers; never auto-save a candidate. A name_candidates_note on a pre-0.3.1 video job explains that its legacy flat OCR may not yield hints. The job remains readable; regenerate only when useful, with force=true, diarize=true, so old identities become pending review instead of being lost. diarize=true needs the [diarization] extra — its absence produces an actionable install-hint error.
• Findings/quotes must cite the narrator's exact words + t_ms (+ t_wall when known) + the frame files you actually inspected.
• Low STT/vision confidence → surface a question; never silently guess.
• Any narration language works (Whisper auto-detects; the summary reports language + language_probability). Garbled transcript or low/wrong detection → re-call process_media(path, model="large-v3-turbo", force=true) (best multilingual quality) or pin language="…"; domain jargon → pass vocabulary="Term1, Term2".
• Write digests/summaries for the recording author in the narrator's language; keep quotes verbatim in the original — translate in your own prose only, never inside a quote.