All Services

Computes numeric distribution statistics and category frequency counts for up to 200 dataset storage records (used_mb, row_count fields) using local analytical engines.

Computes numeric distribution statistics and daily sum time series for dataset storage records (used_mb, row_count) from up to 200 supplied rows.

Computes numeric distribution statistics and an equal-width histogram for dataset storage records (used_mb, row_count) across up to 200 supplied rows.

Computes numeric distribution statistics and IQR-based outlier flags for used_mb and row_count fields in dataset storage records (up to 200 rows, 2 reports per call).

Computes a numeric distribution report and a two-measure correlation report for dataset storage records (used_mb, row_count) from up to 200 supplied rows.

Filters dataset storage rows (used_mb or row_count) by a numeric interval and computes a base-100 index series for up to 200 supplied rows, returning two analytical reports.

Filters dataset storage records (used_mb, row_count) by a numeric interval and produces an equal-width histogram, returning two analytical reports for up to 200 rows.

Filters dataset storage records by a numeric interval and flags IQR-based outliers for used_mb or row_count fields across up to 200 rows.

Analyzes dataset storage records (used_mb, row_count) by producing two reports: numeric row ordering and adjacent row changes, for up to 200 rows.

Performs two analytical reports on dataset storage records: numeric row ordering by a chosen metric and a date-by-category pivot table, for up to 200 rows of supplied data.

Computes two analytical reports (numeric row ordering and equal-width histogram) on dataset storage metrics (used_mb, row_count) for up to 200 supplied rows.

Sorts dataset storage records by a numeric field (used_mb or row_count) and flags outliers using the interquartile range (IQR) method, returning two analytical reports for up to 200 rows.

Ranks dataset storage rows by a chosen numeric field (used_mb or row_count) and compares two datasets by shared key, returning two analytical reports for up to 200 rows.

Runs two local analytical reports on dataset storage records: a record quality audit and adjacent-row change detection for used_mb or row_count fields, across up to 200 rows.

Runs a record quality audit and category frequency analysis on up to 200 dataset storage records (used_mb, row_count) and returns two independent analytical reports.

Runs two local analytical reports on supplied dataset storage records: a record quality audit and a daily sum time series for used_mb or row_count metrics, supporting up to 200 rows.

Runs two analytical reports on supplied dataset storage records: a record quality audit and a date-by-category pivot table, for up to 200 rows.

Runs a record quality audit and equal-width histogram analysis on up to 200 dataset storage records (used_mb or row_count), returning two independent analytical reports.

Runs two analytical reports on up to 200 dataset storage records: a record quality audit and exact row deduplication, using local engines on supplied used_mb and row_count data.

Runs three local analytical reports on dataset storage records (used_mb or row_count): record quality audit, exact row deduplication, and adjacent row change detection — no external lookups.

Runs three analytical reports on supplied dataset storage records (used_mb, row_count): a record quality audit, exact row deduplication, and a base-100 index series — all locally, with no external lookups.

Runs three analytical reports on dataset storage records: a record quality audit, exact row deduplication check, and category frequency counts for dataset_group fields — all in one call for up to 200 rows.

Runs three independent analytical reports — record quality audit, exact row deduplication, and daily sum series — on up to 200 dataset storage records (used_mb or row_count fields).

Runs three analytical reports on up to 200 dataset storage records: a record quality audit, exact row deduplication, and a duplicate key report, using local engines with no external lookups.

Runs three analytical reports on dataset storage records (used_mb, row_count): record quality audit, exact row deduplication, and equal-width histogram — all from supplied data, no external lookups.

Runs three analytical reports on dataset storage records (used_mb, row_count): a record quality audit, exact row deduplication check, and IQR-based outlier flagging — all from supplied data, no external lookups.

Runs two analytical reports on supplied dataset storage records: a data quality audit and a grouped numeric summary for used_mb or row_count fields, for up to 200 rows.

Audits record quality and computes a secondary-measure weighted mean for up to 200 dataset storage records (used_mb, row_count) in two independent analytical reports.

Audits data quality and normalises strings for up to 200 dataset storage records (used_mb, row_count), returning two independent analytical reports.

Runs three analytical reports on up to 200 dataset storage records: a record quality audit, string normalisation, and adjacent row change detection for used_mb or row_count fields.

Runs three analytical reports on dataset storage records — record quality audit, string normalisation, and category frequency analysis — for up to 200 rows of used_mb and row_count data.

Runs three local analytical reports (record quality audit, string normalisation, and daily sum series) on up to 200 dataset storage rows containing used_mb and row_count fields.

Runs three analytical reports (record quality audit, string normalisation, and date-by-category pivot) on up to 200 rows of dataset storage records containing used_mb and row_count fields.

Runs three local analytical reports on supplied dataset storage records (used_mb, row_count): record quality audit, string normalisation, and duplicate key detection — up to 200 rows, no external lookups.

Audits quality, normalises strings, and deduplicates rows in dataset storage records (used_mb, row_count) — three analytical reports per call, up to 200 rows.

Runs three analytical reports on dataset storage records (used_mb, row_count): a record quality audit, string normalisation, and IQR-based outlier flagging — up to 200 rows per call.

Runs three local analytical reports on dataset storage records: a record quality audit, string normalisation, and a numeric interval filter for used_mb or row_count fields.

Runs three bundled analytical reports on up to 200 dataset storage records: record quality audit, string normalisation, and secondary-measure weighted mean for used_mb or row_count fields.

Runs two local analytical reports on dataset storage records: a record quality audit and a trailing mean series for used_mb or row_count fields, for up to 200 rows.

Computes a secondary-measure weighted mean and a date-by-category pivot table for up to 200 dataset storage records (used_mb or row_count).

Computes a secondary-measure weighted mean and performs a keyed comparison between two dataset storage record sets (used_mb, row_count) for up to 200 rows each.

Analyzes up to 200 dataset storage records (used_mb, row_count) to produce string normalization and category frequency reports using local analytical engines.

Normalises string fields and flags IQR-based outliers in dataset storage records (used_mb, row_count) for up to 200 rows, producing two independent analytical reports.

Analyzes up to 200 dataset storage records, producing a string normalization report and a numeric distribution report for either used_mb or row_count fields.

Computes trailing moving average series and base-100 indexed series for dataset storage metrics (used_mb, row_count) from up to 200 supplied rows.

Computes trailing moving-average series and fills missing values in dataset storage records (used_mb, row_count) for up to 200 rows, returning two analytical reports.

Computes trailing moving-average series and IQR-based outlier flags for dataset storage metrics (used_mb, row_count) across up to 200 supplied records.

Computes a trailing mean time series and a keyed comparison between two supplied dataset storage record sets (used_mb, row_count) for up to 200 rows.

Computes a trailing moving average series and a secondary-measure weighted mean for dataset storage records (used_mb, row_count) supplied by the caller.

Computes Pearson correlation between used_mb and row_count, and fills missing values using a chosen strategy (zero, mean, or median), for up to 200 dataset storage records.

Computes two-measure correlation (used_mb vs row_count) and performs keyed dataset comparison between two supplied dataset storage record sets, returning two analytical reports for up to 200 rows each.

Computes Pearson correlation between used_mb and row_count, plus a weighted mean for a secondary measure, across up to 200 dataset storage records in a single call.

Computes a base-100 indexed time series from up to 200 supplied data validation result records (checked vs. failed records per rule and date), returning local descriptive analytics with no enrichment or forecasting.

Computes category (rule) frequency counts from a batch of data validation records, returning a descriptive breakdown of how often each rule/category appears across supplied validation results.

Computes daily sum time-series statistics for data validation results (checked_records or failed_records) across up to 200 typed records, grouped by date and rule.

Pivots data validation results (checked and failed records) by date and rule category, producing a cross-tabulated summary report for up to 200 input rows.

Analyzes up to 200 data validation records to identify duplicate keys across rules and dates, returning a bounded duplicate key report.

Computes an equal-width histogram over checked_records or failed_records fields from up to 200 data validation result rows, returning a local descriptive analytics report.

Removes exact duplicate rows from a batch of data validation result records and returns a deduplicated report.

Fills missing numeric values in data validation result records (checked_records or failed_records) using a chosen imputation method (zero, mean, or median).

Compares two keyed datasets of data validation results (checked and failed records per rule per date) and returns a descriptive analytics comparison report.

Filters a batch of data validation result records (by date, rule, and record counts) to only those rows where a chosen numeric field falls within a specified min/max interval.

Computes a secondary-measure weighted mean over up to 200 data validation result records (checked/failed records per rule and date), returning a local descriptive analytics report.

Normalises and standardises string fields across up to 200 data validation result records, producing a bounded descriptive analytics report.

Computes a rolling/trailing mean time series over data validation metrics (checked_records or failed_records) grouped by rule, for up to 200 supplied records.

Classifies a given text into user-specified categories using AI-powered text processing

Expands or elaborates a short piece of text into a longer version using AI-powered text processing, with optional style and length controls

Generates a QR code from a given text string, with optional size and format parameters

Encodes or decodes text using Base64 encoding via a pay-per-call API

Converts a block of text into a concise, formatted bullet-point list using AI-powered text processing

Fetches trivia questions of a specified type and quantity on demand, paid per-call with USDC

Searches for available hotels at a destination for given check-in/check-out dates and occupancy, returning hotel listing data.

Formats and pretty-prints a JSON string with optional indentation control

Summarizes a given block of text into a concise output using AI-powered processing, with optional style and length controls

Generates a random string of a specified length and encoding on demand

Rewrites or paraphrases input text using AI with optional tone and style controls

Takes a block of text and returns an AI-generated explanation, simplified or deepened based on specified depth and audience.

Validates an IP address and returns structured information about it, such as format validity and metadata

Extracts the most relevant keywords from a given text using AI-powered NLP processing

Computes a cryptographic hash of the provided text using a specified algorithm

Checks and corrects grammar in a given text using AI-powered text processing

Encodes or decodes text using Base64 encoding via a pay-per-call API

Fetches a joke on demand, with optional category filtering and safe-mode toggle

Simplifies complex text to a specified reading level using AI-powered text processing

Analyzes the sentiment of a given text string, returning a structured sentiment result via AI-powered text processing.

Fetches live cryptocurrency price data for a specified coin against a target currency

Returns color metadata and information for a given HEX color code

Translates text from one language to another using AI-powered processing, with optional source language specification

Fetches real-time or current stock market data for a given ticker symbol

Rewrites input text into one or more paraphrased variations using AI-powered text processing

Returns one or more random cat images on demand, pay-per-call via USDC on Base.

Searches live public web pages for current data warehouse pricing information specifically relevant to agent builders, returning source links and text snippets.

Searches live public web pages for data warehouse pricing information tailored to analyst use cases, returning source links and snippets.

Searches live public web pages for data warehouse pricing information targeted at developers, returning source links and text snippets.

Searches live public web pages for data warehouse pricing information specifically relevant to operations teams, returning source links and snippets.

Searches live public web pages for data warehouse pricing information targeted at technology buyers, returning source links and snippets.

Returns runtime and platform compatibility constraints for the date-fns npm package (or a specified version) from the npm registry.

Retrieves published artifact integrity metadata (checksums, hashes, signatures) for the dayjs npm package to support integration and upgrade planning.

Returns runtime and platform compatibility constraints for the dayjs npm package, including engine requirements, OS support, and integration metadata for a given version.

Calculates the Pearson correlation between computed Days Inventory Outstanding (DIO) values and a set of user-supplied benchmark figures.