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Inspiration is the research layer. It reads the accounts you track, the outliers (top-performing content) from them, and your own brand-account performance. It is the grounding the pipeline draws on before any draft.

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Operations

All reads need brandkit:read.

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Scoping to a brand kit

Most reads accept a brandKitId so you see only the accounts and outliers linked to one brand. list_outliers --brand-kit <id> (CLI) or the brandKitId argument (MCP) narrows the pool. If a scoped read comes back empty, the brand kit may have no linked accounts; check get_brand_kit for its inspirationAccounts and brandAccounts. Accounts are linked to a brand kit in the app.

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Mining outliers (the two-step)

Outliers carry the signal that makes a draft perform. List them, then go deep on the best.
1

List

list_outliers (scoped with brandKitId) to find the top performers. Each carries performance signals.
2

Get content

get_inspiration_content on the top few for the full detail: transcript, hashtags, and the structure you want to learn from (hook archetype, pacing, CTA).
This two-step is the Ground phase of the pipeline. The patterns you extract here are what the host LLM emulates, in the user’s own voice, when it drafts.