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Computes benchmark correlation for token position price stress, comparing observed price-change outcomes against user-supplied benchmark thresholds using a deterministic calculation.

Computes a Base-100 rebase index comparing a token position's USD value against its fully diluted valuation (FDV) across multiple observations, using deterministic calculation.

Computes a statistical distribution summary of token position size relative to fully-diluted valuation (FDV) across a batch of observations.

Computes the local finite-change elasticity of a token position relative to its fully diluted valuation (FDV), given a baseline position and FDV in USD and a percentage step size.

Performs ordered trend analysis on a time-series of token position size versus fully diluted valuation (FDV) observations to identify directional patterns.

Ranks a batch of token position-to-FDV observations by their ratio, producing a deterministic record-level ranking report for supplied data only.

Computes a rolling mean of the ratio between a token position's USD value and its fully diluted valuation (FDV) across a supplied time series of observations.

Computes a weighted arithmetic mean of token position-to-FDV ratios from a supplied set of observations

Calculates the marginal change in token dilution fraction caused by an unlock event, using sequential before/after input attribution to isolate each input's contribution.

Computes a base-100 rebased index comparing quoted token amounts against actually received token amounts across a batch of observations, flagging shortfall magnitude deterministically.

Calculates the local finite-change elasticity between a token quote amount and the received amount, measuring how sensitive the shortfall ratio is to small percentage changes in one of those values.

Compares quoted token amounts against received token amounts across named scenarios to compute shortfall metrics deterministically.

Computes a rolling mean of token quote shortfall (quoted minus received token amounts) over a sliding window across a series of observations.

Computes a sequential input-change attribution (waterfall) report comparing before/after token quote and received amounts to explain shortfall changes.

Classifies whether the shortfall between quoted and received token amounts falls within or outside a caller-defined threshold range, using deterministic calculation.

Generates a deterministic two-dimensional scenario grid comparing quoted token amounts versus received token amounts across user-specified value ranges to compute shortfall stress metrics.

Computes a weighted mean of token quote shortfall (quoted tokens minus received tokens) across multiple trade observations

Checks whether a given token or credential is safe, valid, or compromised

AI-powered NFT intelligence combining on-chain data and LLM analysis to provide insights on NFT collections and tokens

AI-powered DeFi analysis endpoint combining CoinGecko and DexScreener data with MiMo LLM to deliver crypto market intelligence

Returns a list of supported cryptocurrencies with optional currency conversion context via AI-powered crypto intelligence

Provides AI-powered NFT analysis by combining CoinGecko and DexScreener data with MiMo LLM intelligence to deliver crypto NFT insights

Returns AI-powered analysis and data on cryptocurrency exchanges using CoinGecko, DexScreener, and MiMo LLM intelligence

Fetches AI-powered cryptocurrency market data and analysis using CoinGecko, DexScreener, and MiMo LLM, comparing a token against a specified quote currency or asset.

Calculates local finite-change elasticity of token sell proceeds shortfall, measuring how proportionally a sell proceeds gap changes relative to small percentage-point shifts in a given input field.

Compares named scenarios for token sell proceeds shortfall, calculating how much actual USD sale proceeds fall short of expected proceeds across up to 20 labelled scenario overrides.

Computes a one-input sensitivity table showing how token sell proceeds shortfall varies as a single input (expected or actual USD) changes across a range of values.

Analyzes a sequence of token sell transactions to identify ordered trends in the shortfall between expected and actual USD proceeds.

Ranks a batch of token sell observations by their proceeds shortfall (expected vs actual USD received), returning a deterministic ranking report.

Calculates the gap between expected and actual USD proceeds from a token sell, determining how far short the outcome fell from a target threshold.

Classifies whether token sell proceeds shortfall (expected vs actual USD received) falls within a caller-defined threshold range, using a deterministic calculation on supplied rows.

Computes a weighted mean of token sell proceeds shortfall values (expected vs actual USD) across multiple trade observations.

Computes a distribution histogram of the rule-flag fraction (flagged_rules / evaluated_rules) across a batch of token observations, bucketed into configurable bins.

Computes a statistical distribution summary (min, max, mean, percentiles, etc.) of the rule-flag fraction (flagged_rules / evaluated_rules) across a batch of supplied token observations.

Computes IQR-based outlier flags for the fraction of flagged rules among supplied token observations, returning a deterministic statistical verdict per row.

Computes and compares the fraction of flagged rules to evaluated rules for a token across named what-if scenarios against a baseline, returning deterministic verdicts.

Computes a rolling mean of the fraction of flagged rules out of evaluated rules across a sequence of token-supplied observations

Calculates the fraction of evaluated token supply rules that have been flagged, returning a deterministic ratio from flagged_rules and evaluated_rules inputs.

Classifies whether the fraction of flagged rules out of evaluated rules for token observations falls within a specified threshold range, returning a bounded verdict for each row.

Computes the weighted mean of the flagged-rules fraction (flagged_rules / evaluated_rules) across multiple token observations supplied by the caller.

Computes the local finite-change elasticity of token supply relative change given a baseline old/new supply and a step percentage.

Analyzes token supply snapshot records to produce category frequency distributions and fills missing values in circulating_units or total_units fields using zero, mean, or median imputation.

Computes a daily sum time series and a keyed dataset comparison for up to 200 token supply snapshot records (circulating_units, total_units) using local analytical engines.

Computes a daily sum time series and a secondary-measure weighted mean for user-supplied token supply snapshot records (circulating_units, total_units), returning two analytical reports.

Analyzes up to 200 token supply snapshot records to produce a duplicate key report and a numeric distribution analysis for circulating_units or total_units.

Analyzes up to 200 token supply snapshot records to produce a duplicate key report and a numeric row ordering report for circulating_units or total_units fields.

Compares two token supply snapshot datasets (circulating_units or total_units) by key and fills missing values explicitly, returning 2 analytical reports for up to 200 rows.

Analyzes token supply snapshot data by computing numeric distribution statistics and adjacent row changes for circulating_units or total_units fields across up to 200 rows.

Analyzes token supply snapshot records (circulating_units, total_units) by generating a numeric distribution report and a Base-100 indexed time series for up to 200 rows.

Analyzes token supply snapshot records (circulating_units, total_units) producing a numeric distribution report and a date-by-category pivot table for up to 200 rows.

Analyzes up to 200 token supply snapshot records by computing numeric distribution statistics and a two-measure correlation report for circulating_units and total_units.

Filters token supply snapshot rows by a numeric range on circulating or total units, then produces a date-by-category pivot report, returning up to 2 analytical reports for up to 200 rows of supplied data.

Filters token supply snapshot rows by a numeric interval on circulating_units or total_units, then fills missing values using zero, mean, or median imputation, returning up to 2 analytical reports for up to 200 rows.

Applies numeric interval filtering and keyed cross-dataset comparison to two sets of token supply snapshot records (circulating_units, total_units), returning up to 2 analytical reports for up to 200 rows each.

Filters token supply snapshot rows by a numeric range on a chosen field and computes a secondary-measure weighted mean, returning up to 2 analytical reports for up to 200 rows.

Computes numeric row ordering and daily sum time series for token supply snapshot records (circulating_units, total_units) across up to 200 rows in two bundled reports.

Runs three analytical reports on user-supplied token supply snapshot records: a record quality audit, exact row deduplication, and a trailing mean time series for circulating_units or total_units.

Audits token supply snapshot records for data quality and compares two keyed datasets of circulating/total unit figures, returning two analytical reports for up to 200 rows.

Audits token supply snapshot records for quality issues and orders rows numerically across circulating_units or total_units fields, returning two analytical reports for up to 200 rows.

Runs three local analytical reports on up to 200 token supply snapshot rows: a record quality audit, string normalisation, and category frequency analysis of circulating/total units data.

Audits, normalises, and deduplicates up to 200 rows of token supply snapshot data (circulating_units, total_units), producing 3 independent analytical reports locally without any external lookups.

Runs three local analytical reports on user-supplied token supply snapshot rows: record quality audit, string normalisation, and numeric interval filter for circulating_units or total_units fields.

Runs three local analytical reports (record quality audit, string normalisation, and secondary-measure weighted mean) on up to 200 token supply snapshot rows containing circulating_units and total_units fields.

Computes a secondary-measure weighted mean and fills missing values in token supply snapshot records (circulating_units, total_units) for up to 200 rows, returning 2 analytical reports.

Computes a secondary-measure weighted mean and performs a keyed dataset comparison on two sets of token supply snapshot records (circulating_units, total_units) for up to 200 rows.

Runs two analytical reports on token supply snapshot rows: string normalisation of instrument_group fields and adjacent row change detection for circulating_units or total_units values.

Applies string normalisation and base-100 index series transformations to user-supplied token supply snapshot records (circulating_units, total_units), returning two analytical reports for up to 200 rows.

Runs string normalisation and category frequency analysis on up to 200 token supply snapshot records (circulating_units, total_units), returning two local analytical reports.

Normalises string fields and computes grouped numeric summaries for up to 200 token supply snapshot records (circulating_units, total_units), producing two analytical reports.

Runs string normalisation and numeric distribution analysis on up to 200 token supply snapshot records (circulating_units, total_units), returning two analytical reports.

Normalises token supply snapshot data and computes correlation between circulating_units and total_units across up to 200 rows, returning two analytical reports.

Validates a CSV of token swap records and identifies blank rows, up to 1000 rows.

Validates a CSV of token swap records by checking that a specified column's values fall within an allowed category list, returning a pass/fail report.

Validates cell length constraints in a CSV table of token swap records, auditing up to 1000 rows for compliance with declared length limits.

Validates a token swap CSV and returns per-column statistics (count, min, max, mean, categories) for up to 1000 rows.

Validates a CSV file of token swap records against a declared column schema contract, returning a column projection report for up to 1000 rows.

Validates numeric columns in a token swap CSV dataset, checking that values fall within allowed types, ranges, and category constraints — up to 1000 rows.

Validates a CSV table of token swap records (up to 1000 rows) and flags cells containing spreadsheet formula injection patterns.

Validates a CSV file of token swap records for whitespace anomalies, checking up to 1000 rows with support for quoted cells and configurable delimiters.

Ranks multiple token trade observations by their trade size relative to quoted liquidity (trade/liquidity ratio), returning a deterministic record ranking report.

Calculates the target gap between a token trade size and quoted liquidity depth, returning a deterministic verdict based on supplied trade and liquidity values.

Computes a deterministic two-dimensional scenario grid of token trade impact relative to quoted liquidity across varying trade_usd and liquidity_usd input combinations.

Computes period-over-period changes in the token transfer receipt fraction (received/sent ratio) across a sequence of token transfer observations.

Computes a base-100 rebased index of the ratio between received and sent tokens across a batch of transfer observations, with a configurable base row.

Computes and compares the token transfer receipt fraction (received/sent ratio) across multiple named scenarios against a baseline, returning a deterministic verdict for each scenario.

Ranks a batch of token transfer records by their receipt fraction (received_tokens / sent_tokens) using deterministic computation.

Classifies whether the ratio of received-to-sent tokens in transfer rows falls within a user-defined threshold range, using deterministic calculation.

Computes a grid of token transfer receipt fraction values across two variable dimensions (sent_tokens and received_tokens) for scenario analysis

Ranks a batch of token observations by their turnover-to-liquidity ratio (period volume divided by liquidity), returning a deterministic record-level ranking report.

Calculates the gap between a token's current turnover-to-liquidity ratio and a user-specified target value, returning a deterministic verdict report.

Computes period-over-period changes in token unlock notional value (tokens × spot price) across a supplied sequence of unlock observations.

Ranks a set of token unlock observations by notional value (unlock_tokens × price_usd) and returns a deterministic record-level ranking report.

Computes a weighted arithmetic mean of token unlock notional values (tokens × spot price) across up to 200 supplied price/quantity observation rows.

Validates a CSV file of token unlock schedule data and reports blank or empty rows, up to 1000 rows.

Validates a token unlock schedule CSV for cell length violations, checking up to 1000 rows with configurable max cell length and allowed category filters.

Validates a CSV of token unlock schedule data against a declared column contract, projecting and checking up to 1000 rows for structural and value correctness.

Validates a CSV table of token unlock schedule data and flags cells containing spreadsheet formulas, up to 1000 rows.

Calculates the relative (percentage) change in token trading volume between adjacent time periods for a batch of token observations.

Computes a base-100 rebased index of token trading volume relative to market capitalization across a supplied series of token observations.

Computes the correlation between a token's volume-to-market-cap ratio observations and a set of benchmark values, returning a deterministic statistical report.