Find quotes and clip moments
search finds the passage; transcript with start and end returns the exact lines around it with second offsets, which give the verbatim quote and the clip boundaries.
Run each program with arcmira_execute_read; arcmira_describe has the method reference.
When to use
- find a quote, with the source and a timestamp
- the moment a show talked about a topic, to clip or cite
- what a specific person said about a topic, in their words
Pick the entity the user meant
Users give names; filters take ids only (ent_..., UC..., 11-character video ids), and a name where an id belongs throws id_required.
- Resolve the exact name the user said, with their own words about it as
context (a bare "Theo" has none).
best and suggested give the id. A suggested pick is an assumption: the answer names it, with suggested.evidence as the reason.ask: several entities fit, and ask.options goes back to the user. No answer at all: the name is not in the index.
For this task:
- A speaker resolves with
{ type: "person" }; pass the id as speakerIds (who said it). A person or brand the passage is about goes in about. A show goes in channelIds as its UC id.
- People are often named by first name ("Chamath"). Pass what the user said about them as
context; resolve suggests the person when one stands out and asks when several fit.
- The topic words go in
query, never in about. If a filtered search returns no chunks, rerun it with fewer filters before saying nothing was found.
Worked program
Pass each block to arcmira_execute_read as one program (a block marked arcmira_execute_write goes to that tool), with the name swapped for the user's. It opens with the pick: set CONTEXT to the user's own words about the name. When several entities fit it returns ask and runs nothing else. Show those options to the user, then run it again with ID set to the pick. Build date windows from arcmira.daysAgo(n) and arcmira.today().
What one person said about a topic, with clip boundaries
const NAME = "Chamath", CONTEXT = undefined, ID = null; // CONTEXT: the user's own words about the name, never a guess. After an ask, set ID to the picked option's id and run again
const r = ID ? null : await arcmira.resolve(NAME, { type: "person", context: CONTEXT });
const e = r && (r.best ?? r.suggested);
if (r && !e) return { ask: r.ask };
const id = ID ?? e.id;
const assumed = Boolean(r?.suggested), why = r?.suggested?.evidence ?? null;
const hits = await arcmira.search({ query: "Anthropic IPO", speakerIds: [id], limit: 5 });
const moments = [];
for (const c of hits.chunks.slice(0, 2)) {
const moment = { episode: c.video_title, show: c.channel_name, date: c.published_at, url: c.watch_url, speakers: c.speakers_by.map(s => s.name) };
try {
const t = await arcmira.transcript(c.video_id, { start: Math.max(0, c.start_seconds - 10), end: c.start_seconds + 50 });
moment.clip = { start: t.lines[0]?.start ?? c.start_seconds, end: t.lines.at(-1)?.end ?? c.start_seconds + 60 };
moment.lines = t.lines.map(l => `[${Math.round(l.start)}s] ${l.text}`);
} catch (err) {
moment.clip = { start: c.start_seconds, end: c.start_seconds + 60 };
moment.passage = c.text;
moment.transcript = err.code;
}
moments.push(moment);
}
return { speaker: { id, name: e?.name ?? null, assumed, why }, as_of: hits.as_of, ...(moments.length ? { moments } : { none: hits.note ?? null }) };
A good answer
- Names the speaker or show it searched, with the id, and quotes the words exactly as the transcript lines give them, trimmed to whole sentences, never paraphrased inside quotation marks.
- Gives the show, the episode title, the date, and the speaker when the chunk or a premium transcript names one.
- Links the
watch_url, which starts at the moment, and gives a clip start and end in seconds from the transcript lines.
- Says so when nothing matched, with
as_of, instead of offering a quote from memory.
Traps
search ranks by the words in query; put the distinctive words of the phrase there, not a paraphrase.- A chunk found with
speakerIds also holds other people's lines. Quote only lines that start with the speaker's name ("Chamath Palihapitiya: ..."), never the whole passage.
- Caption lines are machine text: fix nothing inside the quotation marks. A premium transcript (
quality: "premium", paid plans) adds speaker ids into speakers[].
- A caption read uses 4 credits per started 15 minutes of its
start/end window, and of the whole episode without one. A Premium read uses credits for the whole video whatever the window.
- Some videos have no readable caption track yet (
transcript_unavailable, transcript_fetching); quote the search chunk text for those and say the words come from the search passage.
Docs: https://arcmira.com/docs/mcp-server
After the answer
Monitors can follow what a research turn found, and each alert uses 25 credits from the account's plan, then its on-demand budget. The company-watch skill sets one up.
If anything was wrong, slow, or missing for the user, send one arcmira_feedback.