sponsor-research

Sponsor research

Two directions. A show to its sponsors: arcmira.sponsors(channelId) ranks recurring sponsors by ad reads with first and last seen dates. A brand to the shows it sponsors: arcmira.recommendations(entityId, { kind: "sponsored" }) lists each ad read, which the program groups by show.

Run each program with arcmira_execute_read; arcmira_describe has the method reference.

When to use

• who sponsors a show, how many ad reads, since when, still active
• which shows a brand sponsors or advertises on, and how often
• sponsors two shows share (sponsors of each, then intersect by entity.id)

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.

1. Resolve the exact name the user said, with their own words about it as context (a bare "Theo" has none).
2. best and suggested give the id. A suggested pick is an assumption: the answer names it, with suggested.evidence as the reason.
3. 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 show resolves with { type: "channel" }; its id is youtube_channel_id (a UC id).
• A brand resolves with no type (a company can be typed product). A sponsor is a company: a person or a topic with the same name ("Freddie Mercury") is not the brand.
• When two company rows compete, the one with ad reads is the sponsor: run the brand program for each and keep the one with reads.

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().

A show's sponsors

const NAME = "TBPN", 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 channel_id and run again
const r = ID ? null : await arcmira.resolve(NAME, { type: "channel", context: CONTEXT });
const e = r && (r.best ?? r.suggested);
if (r && !e) return { ask: r.ask && { question: r.ask.question, options: r.ask.options.map(o => ({ ...o, channel_id: r.candidates.find(c => c.id === o.id)?.youtube_channel_id ?? null })) } };
const id = ID ?? e.youtube_channel_id;
const assumed = Boolean(r?.suggested), why = r?.suggested?.evidence ?? null;
const s = await arcmira.sponsors(id);   // limit is a Pro+ filter; slice instead
return {
  show: s.channel.name, channel_id: id, page: s.channel.page, assumed, why, sponsors_total: s.meta.total,
  sponsors: s.sponsors.slice(0, 10).map(x => ({ name: x.entity.name, id: x.entity.id, page: x.entity.page, ad_reads: x.ad_reads, episodes: x.videos, first_seen: x.first_seen, last_seen: x.last_seen, status: x.sponsor_status?.status ?? null })),
};

The shows a brand sponsors, last 90 days

const NAME = "Mercury", 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, { 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 after = arcmira.daysAgo(90);
const reads = [];
let cursor, entity, window;
do {
  const page = await arcmira.recommendations(id, { kind: "sponsored", after, limit: 100, cursor });
  entity = page.entity;
  window = page.window;
  reads.push(...page.recommendations);
  cursor = page.has_more ? page.next_cursor : undefined;
} while (cursor && reads.length < 500);
const shows = new Map();
for (const x of reads) {
  const name = x.media.source_channel?.name ?? x.media.channel_id;
  const row = shows.get(name) ?? { show: name, channel_id: x.media.channel_id, ad_reads: 0, episodes: new Set(), latest: "" };
  row.ad_reads += 1;
  row.episodes.add(x.media.video_id);
  if (x.media.published_at > row.latest) row.latest = x.media.published_at;
  shows.set(name, row);
}
return {
  brand: { id, name: entity?.name ?? e?.name ?? null, type: entity?.type ?? e?.type ?? null, assumed, why }, window, ad_reads_total: reads.length,
  shows: [...shows.values()].sort((a, b) => b.ad_reads - a.ad_reads).slice(0, 10).map(s => ({ ...s, episodes: s.episodes.size })),
  sample_read: reads[0] ? { said: reads[0].verbatim_quote, show: reads[0].media.source_channel?.name ?? null, date: reads[0].media.published_at, promo_code: reads[0].promo_code } : null,
};

A good answer

• Names the brand or show it used, with its type and id, and any look-alike it set aside.
• Ranks sponsors (or shows) by ad reads and gives the counts, first and last seen dates, and the sponsor status.
• States the window and the as-of date, and that counts cover the shows Arcmira indexes.
• Quotes one ad read verbatim with its promo code when there is one, and links each name to the page the result carries.

Traps

• recommendations can return recommendations_not_enabled. Explain the account-access limit and required tier reported by the API; a channel sponsor list does not answer which shows recommend a brand.
• An ad read is sponsored; an unpaid on-air endorsement is kind: "organic". Do not mix them in one count.
• Page with cursor until has_more is false before you count reads; one page is at most 100 rows.

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.