Report whether a CLI token is present and which user it authenticates as.

Check API and database health.

Return the signed-in user's profile (email, username, organization).

List scored tickers with optional filters. Market caps are in USD.

Rows are slimmed for MCP by default (duplicates as ticker strings, description truncated); pass full_rows=True for the raw backend payload.

Return the top tickers by quality score as a {ticker: score} map.

Return the score row(s) for a single ticker.

Return current scores for a specific list of tickers.

Looks up the latest score for each requested ticker under the default quality score, or under a saved scoring system when scoring_system_id is given (which requires being signed in and owning that system). Tickers with no score are omitted from the response. Results are sorted by quality score descending.

Rows are slimmed for MCP by default (duplicates as ticker strings, description truncated); pass full_rows=True for the raw backend payload.

Args: tickers: Exact ticker symbols to score (e.g. ["AAPL", "MSFT", "ASML.AS"]). scoring_system_id: Optional saved scoring-system id; omit for default scoring. full_rows: Return unmodified rows (embedded duplicate rows, full descriptions).

Returns: dict: A ScoreListResponse with data (one row per found ticker), pagination (total_count = number found), and aggregates.

Return min/max/average score statistics for a filtered universe.

Return aggregated total market cap (USD) for a filtered universe.

Compute custom scores from a CustomScoreConfig, scored against a chosen peer group.

The config is a CustomScoreConfig: weighted metric groups (each with weighted metrics) plus scoring parameters winsorizePercentile (1-10), missingDataPercentile (0.1-0.5), normalizeGroupZScores and includeDuplicatesInScoring (booleans). Loose inputs (snake_case keys, legacy winsorize/zScore flags) are normalized automatically.

Scoring happens in two stages, and they answer different questions.

Stage 1 — scoring_universe: who is in the peer group. Quality scores are relative: each company is winsorized and z-scored against a population. Narrowing this changes every score and can reorder the list. Accepts sectors, industries, regions, countries, currencies, exchanges, min_market_cap_usd and max_market_cap_usd. Omit it to score against the whole universe.

Stage 2 — the filter arguments below: which rows come back. Applied after scoring, so they never change a score. They accept the same keys as stage 1, so any filter can be asked either way. OR logic within a filter, AND across filters.

So "best European tech judged against European tech" is scoring_universe={"sectors": ["Technology"], "regions": ["Europe"]}, while "best European tech judged against the world" is sectors=["Technology"], regions=[...] as stage-2 arguments. The response's scoring_universe field reports the peer group and its size — scores computed against different peer groups are not comparable, so do not mix them in one table.

Two things do not fit the split cleanly, and both are deliberate:

• min_market_cap_usd as a stage-2 argument also floors the scoring population (a long-standing backend behaviour). Set min_market_cap_usd inside scoring_universe to control the peer group explicitly; it overrides the stage-2 floor. max_market_cap_usd filters rows only unless set in scoring_universe.

• min_score/max_score/ticker/tickers cannot appear in scoring_universe: the first two filter on the scores being computed, the rest select rows. Passing them there returns an error rather than being ignored.

A nested config['filters'] block is saved-screen state, persisted by screen_share/systems_create/systems_update so a screen restores its view. Passing a saved config here applies that block as stage-2 filters (an explicit argument below wins), matching what the web app does — it never defines the peer group.

Create a shareable link for a screen (CustomScoreConfig) and return its URL.

Persists the screen configuration and returns a short, public link that can be copy-pasted to anyone — recipients open it to view and load the exact screen. The link is content-addressed: sharing an identical config returns the same URL instead of creating a duplicate.

Args: config: The complete CustomScoreConfig to share — weighted metric groups, scoring parameters (winsorizePercentile, missingDataPercentile, normalizeGroupZScores, includeDuplicatesInScoring), and an optional nested filters block. Loose inputs (snake_case keys, legacy winsorize/zScore flags, or top-level filter keys) are normalized to this shape so the shared link always renders.

Returns: dict with url (the shareable link to copy-paste), slug (the short identifier), created (True if newly created, False if an identical screen already existed), and view_count.

Return available filter values (sectors, industries, countries, currencies, exchanges).

Return available tickers, optionally truncated to limit.

Search available tickers by case-insensitive substring.

Return score history for a single ticker (dates: YYYY-MM-DD).

Return score history for several tickers at once.

Fetch the top-N tickers and return their score history.

List the user's saved scoring systems.

Show a saved scoring system by ID.

Create a saved scoring system from a config object.

Update a saved scoring system.

Delete a saved scoring system.

Apply a saved scoring system (increments its usage count).