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AI media pipeline

Wire agents into your media library: search by meaning, pull references, generate variations, and file the results back — automatically. Two paths: call the API directly, or point any MCP client at the hosted server.

An AI media pipeline using the Playbook MCP server

The flow​

  1. Agent searches semantically for references (AI search).
  2. Agent ingests generated variations in bulk.
  3. Agent auto-tags them with custom fields.
  4. Or skip the glue code entirely and use the MCP server.

1. Semantic search for references​

AI search returns meaning-ranked results with a cursor for paging deeper:

agent.ts
const ORG = "your-org";
const TOKEN = process.env.PLAYBOOK_TOKEN!;

async function aiSearch(query: string, cursor?: string) {
  const params = new URLSearchParams({ query });
  if (cursor) params.set("after_cursor", cursor);
  const res = await fetch(
    `https://api.playbook.com/v1/${ORG}/ai_search?${params}`,
    { headers: { Authorization: `Bearer ${TOKEN}` } },
  );
  return res.json(); // { data: [...assets], after_cursor?: "..." }
}

const refs = await aiSearch("brand palette: warm coral, moody lighting");

2. Ingest generated variations​

Your model produces new images (hosted somewhere reachable). Add them in one call — Playbook fetches and processes each in the background:

ingest.ts
async function ingestVariations(boardToken: string, urls: string[]) {
  const res = await fetch(
    `https://api.playbook.com/v1/${ORG}/assets/batch_create_from_urls`,
    {
      method: "POST",
      headers: {
        Authorization: `Bearer ${TOKEN}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({
        batch: {
          collection_token: boardToken,
          assets: urls.map((uri, i) => ({
            uuid: `gen-${i}`,
            uri,
            title: `Variation ${i + 1}`,
          })),
        },
      }),
    },
  );
  return (await res.json()).data; // [{ uuid, asset }] — each starts as a skeleton
}

3. Auto-tag the results​

Have the agent file each variation against your taxonomy:

tag.ts
async function tag(assetToken: string, fields: Record<string, string>) {
  await fetch(`https://api.playbook.com/v1/${ORG}/assets/${assetToken}/update`, {
    method: "PATCH",
    headers: {
      Authorization: `Bearer ${TOKEN}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({ asset: { fields } }),
  });
}

await tag("variation-1", { "Approval Status": "In Review", Source: "AI" });

4. Or skip the code — use MCP​

If your agent runs in Claude, Cursor, or ChatGPT, you don't need any of the above. Point it at the hosted MCP server and it can search, tag, upload, and organize your media in natural language:

claude mcp add playbook --transport http https://mcp.playbook.com \
  --header "Authorization: Bearer $PLAYBOOK_TOKEN"

See the MCP guide for Cursor and ChatGPT setup and the full tool list.