Rayana AI

Latest ranked candidate batch for a lane: daily (the main research lane), turtle (breakout system), or mr (mean-reversion). Rows come back in rank order, best first, so the default page is the top of the batch. `total` in the response is the batch size; when it exceeds `count`, later rows are reached with `offset`. `include_assessment` adds each row's technical detail (pattern and cluster validation, volume profile) and roughly triples the response; `rayana_ai_ticker` returns the same detail for a single name.

Which lanes (daily/turtle/mr) currently have a batch available.

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Latest aggregator-consensus batch for one asset lane: which tickers cleared the signal pipeline, blended agg_score, confirming source count.

Per-source consensus snapshots (insider trades, congressional trades, earnings beats, dated catalysts, on-chain flows, 13F filings, AI news-beneficiary reads). Omit `source` for every source at once, or pass one of the slugs returned in a rankings/signals row's source list. `limit` caps rows per source. The sources are very unevenly sized — institutional holdings runs to ~1700 rows where on-chain flows may have two — so a source that comes back with exactly `limit` rows probably has more, and narrowing with `source` and raising `limit` reads that one layer deeply for far less context than raising `limit` across all seven.

Full per-ticker research view: score, lane membership, per-source signals, virality, consensus, cluster-rotation position, forward returns.

Daily close-price history for one ticker over the given period. Long periods are evenly sampled down to `max_points` bars rather than cut short, so the series still spans the whole period — `5Y` and `MAX` return the shape of the history, not its last year. `last_close`, `change_pct` and the `range_low`/`range_high` extremes are computed over every bar regardless of sampling.

The ticker's current AI analyst read (lean, conviction, thesis, six fixed sections, evidence for/against, invalidation condition). Never triggers a new model call; 404s when the ticker has no current read. Counts one read against the subscription's monthly allowance, once per ticker per day; `rayana_ai_analyst_read` also reports what remains.

Open a ticker's AI analyst read: the current one, or a new one generated on the spot when the ticker has none (this can take about half a minute). Each ticker opened counts one read against the subscription's monthly allowance, once per day; re-opening it the same day is free. `quota` reports reads used and remaining this month and the reset date. `books` lists the Rayana AI model portfolios on the subscription's plan that hold the ticker (weight, since when, last change), and `locked_count` how many books on higher plans hold it. Errors for tickers outside the covered universe and when the allowance is used up.

Answer one preset follow-up question about a ticker's current AI analyst read: trend (is the trend intact), break (what would break the thesis), macro (exposure to macro and rates), smart_money (insiders and smart money), earnings (the earnings setup), changed (what changed since the last read). Answers come from the read and its data only and are the same for everyone. Counts like opening the read, once per ticker per day; `quota` reports reads remaining this month.

Two tickers' current AI analyst reads side by side: a table of each read's lean, conviction and the direction of its six sections, and a short descriptive comparison of where the reads agree and differ. It never says which one is better or what to do. Opens both reads (generating one if a ticker has none, about half a minute each) and counts as two reads, once per ticker per day; `quota` reports reads remaining. Comparisons are the same for everyone and cached per pair of reads.

One ticker's published article history, newest first.

Cluster-rotation layer. `summary`: latest per-cluster RRG quadrant/rank rows with deltas, plus the most recent quadrant-crossing events (`days` controls the window both are computed over). `laggards`: members trailing a firing cluster's leader. `discovered`: unsupervised cluster-discovery cohorts the structural taxonomy misses. `members`: structural cluster membership. On `summary`, two blocks are trimmed because together they are 80% of the response while answering a narrower question than the rows do. `include_trajectory` adds each cluster's full rank series — a chart payload, off by default. `events` returns the newest 25 crossings; if exactly 25 come back there are probably older ones, and a wider `days` is not what returns them.

The attention leaderboard: latest virality snapshot ranked by VScore, with the full intensity/breadth/slope/vramp decomposition.

The attention layer's raw texture. `sources`: per-source reach today. `heat`: per-(ticker, source) attention contributions. `search`: rising/breakout consumer search queries. `video`: rising video-view velocity. `limit` applies to search/video only; omit it to use that view's own default.

The daily market brief: a generic engine-wide read with no recommendations, descriptive only. Latest by default; pass `date` (YYYY-MM-DD) for a specific day.

The economy behind the market, descriptive only. `cycle`: the business-cycle phase (expansion / slowdown / contraction / recovery), the composite and its components, plus recent month-ends. `earnings`: S&P 500 (`index="spx"`) or Nasdaq-100 profit growth by quarter (sum and median, net and operating income), breadth, beat rate and coverage, including the quarter in season. `indicators`: the FRED series behind the cycle with source citations. `history` sets the window (month-ends, quarters or observations); omit it for the default.

The portfolio-analyst brief with its per-ticker views — the broadcast opinion layer, identical for every caller.

The full shelf of Rayana AI's broadcast model portfolios as merchandising cards, with each card's tier/openable/delayed flags.

One model portfolio's full detail: holdings, overlay legs, event feed, NAV series, and windowed returns. Returns an error if the book's tier (e.g. an Elite-only book) is above the caller's own subscription tier.

Resolve free text (ticker or company name) to known Rayana AI symbols, best match first. Pure resolution — never accrues signal.

The public articles feed, newest first, when `slug` is omitted; one published article's full body when `slug` is given.

Rayana AI's subscription tiers: price, rate limits, and what each tier includes. Works without signing in or an API key. The research tools on this server require the Pro tier or higher. Prices are USD; annual billing is ten months' price for twelve. `rayana_ai_start_checkout` returns a subscription link for a tier listed here.

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Returns a subscription link for a tier, which the user opens in a browser. Works without signing in or an API key. The tool itself takes no payment: the user signs in, accepts the Terms of Service and pays on Stripe's hosted checkout page. Standard does not include access to this server's research tools; Pro and Elite do. After payment, a connection that signs in with OAuth (as Claude's connectors do) reaches the research tools once the user signs in with the same account. A client that connects with an API key instead gets one through a pairing code on the account page (see `rayana_ai_complete_setup`).

For clients that connect with an API key rather than OAuth: exchanges a pairing code from the user's account page for an API key. Destructive: it issues a new key and immediately revokes the account's previous one, so any other client still using the old key stops working. OAuth connections are not affected. The key is returned once and cannot be retrieved later; only a hash of it is stored. It is sent as the `X-API-Key` header on MCP and REST requests. Codes are single-use, expire after 15 minutes, and are invalidated after a few wrong attempts.