Categories › Data & Databases › Warehouses & analytics (BigQuery, Snowflake...)
Rank, screen and compare the Hyperliquid perp universe on a completed day.
Remote server: your requests and data go to tesseralytics.dev. Claude Code: claude mcp add --transport http hyperliquid-cross-sectional-analytics-te https://tesseralytics.dev/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
get_universe — The coverage picture: the last complete interval, how many markets are in the universe for a scope, how much history sits behind them, and hscreen_universe — Screen the whole market on ONE completed interval with several conditions ANDed together. Pick bar (1d, the default, or 1h for completed houfunding_divergence — Rank markets by how far their funding has diverged from a benchmark (default ETH) over a trailing window. Pick bar (1d, the default, or 1h fquery_cross_section — Rank the whole market on ONE completed interval: pick bar (1d, the default, or 1h for completed hours), columns, an order_by field and top_nlist_datasets — List the downloadable datasets: one-line summary, the plan required, and the month range available. Open — no key needed. Then use describe_describe_dataset — The full dictionary for ONE downloadable dataset: description plus every column's type, nullability and plain-English meaning. Pass a name fquery_series — The series for one market or a few (max 10): one row per market per interval over a trailing window, ordered by interval then market. Pick blist_fields — Every field the query tools accept, each with its meaning, unit, whether it can be missing, and which bars (1d, 1h) it exists on. Open — no Not tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.