All Services

Deduplicates exact-duplicate rows and flags IQR-based outliers in pipeline duration records (run_seconds and processed_rows), returning two analytical reports for up to 200 rows.

Performs exact row deduplication and numeric distribution analysis on pipeline duration records (run_seconds, processed_rows) for up to 200 rows, returning two independent analytical reports.

Deduplicates pipeline duration records and applies numeric row ordering across up to 200 rows of run_seconds and processed_rows data, returning 2 analytical reports.

Deduplicates supplied pipeline duration records and computes a trailing moving average series over run_seconds or processed_rows for up to 200 rows.

Computes a grouped numeric summary and a base-100 index series for supplied pipeline duration records (run_seconds, processed_rows) across up to 200 rows.

Generates two analytical reports (grouped numeric summary and date-by-category pivot) for pipeline duration records including run_seconds and processed_rows, using up to 200 supplied rows.

Computes grouped numeric summaries and fills missing values for pipeline duration records (run_seconds, processed_rows) across up to 200 rows.

Computes grouped descriptive statistics and IQR-based outlier flags for pipeline duration records (run_seconds, processed_rows) across dataset groups.

Computes a grouped numeric summary and a keyed dataset comparison for pipeline duration records (run_seconds and processed_rows) across up to 200 rows.

Computes a grouped numeric summary and a numeric interval-filtered report for pipeline duration records (run_seconds, processed_rows) supplied by the caller.

Computes a grouped numeric summary and a two-measure correlation analysis for up to 200 pipeline duration records covering run_seconds and processed_rows.

Analyzes pipeline duration records to produce IQR-based outlier flags and a date-by-category pivot table for up to 200 rows.

Analyzes pipeline duration records (run_seconds, processed_rows) to flag IQR-based outliers and fill missing values using a chosen imputation method, producing two analytical reports per call.

Computes IQR-based outlier flags and secondary-measure weighted mean for up to 200 pipeline duration records (run_seconds or processed_rows), returning two independent analytical reports.

Compares two keyed pipeline-duration datasets by row_id and fills missing values explicitly, returning two analytical reports for up to 200 rows.

Generates two statistical reports (numeric distribution and date-by-category pivot) for pipeline duration data (run_seconds and processed_rows) supplied by the caller, for up to 200 rows.

Computes numeric distribution statistics and fills missing values (via zero, mean, or median) for pipeline duration records (run_seconds, processed_rows) across up to 200 rows.

Computes numeric distribution statistics and IQR-based outlier flags for pipeline duration data (run_seconds or processed_rows) across up to 200 supplied rows.

Computes a numeric distribution and a secondary-measure weighted mean for supplied pipeline duration records (run_seconds, processed_rows) using local analytical engines.

Computes a numeric distribution and a trailing mean time series for pipeline duration metrics (run_seconds or processed_rows) from up to 200 supplied records.

Computes a numeric distribution report for pipeline run_seconds and a two-measure correlation report between run_seconds and processed_rows for supplied pipeline duration records (up to 200 rows).

Analyzes pipeline duration records by filtering rows within a numeric range and detecting adjacent row changes, returning 2 analytical reports for up to 200 rows.

Filters pipeline duration records by a numeric interval and computes a Base-100 indexed series for run_seconds or processed_rows, returning two analytical reports for up to 200 supplied rows.

Filters pipeline duration records by a numeric range on a chosen metric and computes a daily sum time series, returning two analytical reports for up to 200 rows.

Runs two local analytical reports on pipeline duration data — a numeric interval filter and a date-by-category pivot — for up to 200 supplied rows of run_seconds and processed_rows data.

Filters pipeline duration records by a numeric interval and flags IQR-based outliers across run_seconds and processed_rows fields, producing two analytical reports for up to 200 rows.

Filters pipeline duration records by a numeric interval and computes a secondary-measure weighted mean, returning two analytical reports for up to 200 rows.

Analyzes pipeline duration records to produce numeric row ordering and adjacent row change reports for run_seconds or processed_rows fields.

Generates two analytical reports for supplied pipeline duration records: numeric row ordering and a base-100 indexed series, for up to 200 rows.

Computes numeric row ordering and daily sum time series for pipeline duration records (run_seconds and processed_rows) across up to 200 rows in two bundled analytical reports.

Generates two local analytical reports for pipeline duration records: numeric row ordering and a date-by-category pivot table, for up to 200 rows of run_seconds and processed_rows data.

Analyzes pipeline duration records by ranking rows numerically and comparing two keyed datasets, producing two analytical reports for up to 200 rows of run_seconds and processed_rows data.

Computes numeric row ordering and secondary-measure weighted mean for pipeline duration records (run_seconds, processed_rows) on up to 200 supplied rows.

Computes numeric row ordering and a trailing moving-average series for pipeline duration records (run_seconds or processed_rows) across up to 200 rows.

Runs two local analytical reports on pipeline duration data: a record quality audit and an adjacent-row change analysis, for up to 200 rows of run_seconds or processed_rows metrics.

Performs two independent local analytical reports on pipeline duration records: a record quality audit and a daily sum time series, covering up to 200 rows of run_seconds or processed_rows data.

Runs two independent analytical reports on pipeline duration records — a record quality audit and a date-by-category pivot — for up to 200 rows of run_seconds or processed_rows data.

Runs two independent analytical reports on pipeline duration records — a record quality audit and a duplicate key check — for up to 200 rows of run_seconds and processed_rows data.

Runs three analytical reports (record quality audit, exact row deduplication, and adjacent row changes) on pipeline duration records (run_seconds, processed_rows) for up to 200 rows.

Runs three independent analytical reports on pipeline duration records: record quality audit, exact row deduplication, and base-100 indexed series — all locally, without external lookups.

Runs three analytical reports on pipeline duration records — a record quality audit, exact row deduplication, and category frequency analysis — for up to 200 rows of run_seconds/processed_rows data.

Runs three bundled analytical reports on pipeline duration records: a record quality audit, exact row deduplication, and an equal-width histogram for run_seconds or processed_rows.

Runs three local analytical reports on pipeline duration records: a record quality audit, exact row deduplication, and IQR-based outlier flagging for run_seconds or processed_rows fields.

Runs three local analytical reports on pipeline duration records: record quality audit, exact row deduplication, and numeric distribution analysis for run_seconds or processed_rows fields.

Runs three independent analytical reports on pipeline duration records — a record quality audit, exact row deduplication check, and numeric interval filter — for up to 200 rows of run_seconds/processed_rows data.

Runs three independent analytical reports — record quality audit, exact row deduplication, and numeric row ordering — on pipeline duration data (run_seconds, processed_rows) for up to 200 rows.

Runs three local analytical reports on pipeline duration records: record quality audit, exact row deduplication, and trailing mean time series for run_seconds or processed_rows.

Audits the quality of pipeline duration records (run_seconds, processed_rows) and fills missing values using a chosen imputation method (zero, mean, or median) for up to 200 rows.

Runs two analytical reports on pipeline duration data: a record quality audit and a grouped numeric summary, for up to 200 rows of run_seconds and processed_rows data.

Performs record quality audit and IQR-based outlier flagging on pipeline duration data (run_seconds or processed_rows) for up to 200 rows, returning two independent analytical reports.

Audits record quality and compares two keyed datasets of pipeline duration metrics (run_seconds, processed_rows) for up to 200 rows, returning two independent analytical reports.

Runs a record quality audit and numeric interval filter on pipeline duration records (run_seconds, processed_rows), returning two analytical reports for up to 200 rows.

Audits data quality and normalises strings for pipeline duration records (run_seconds, processed_rows) across up to 200 rows, returning 2 independent analytical reports.

Runs three analytical reports (record quality audit, string normalisation, and adjacent row change detection) on up to 200 pipeline duration records containing run_seconds and processed_rows fields.

Runs three analytical reports on pipeline duration records — record quality audit, string normalisation, and base-100 index series — for up to 200 rows of run_seconds/processed_rows data.

Runs three local analytical reports on pipeline duration records: record quality audit, string normalisation, and category frequency analysis for up to 200 rows of run_seconds and processed_rows data.

Runs three analytical reports (record quality audit, string normalisation, and date-by-category pivot) on supplied pipeline duration records containing run_seconds and processed_rows fields.

Runs three combined analytical reports (data quality audit, string normalisation, and equal-width histogram) on pipeline duration records containing run_seconds and processed_rows fields.

Runs three independent data-quality reports (quality audit, string normalisation, and exact deduplication) on up to 200 pipeline-duration records containing run_seconds and processed_rows fields.

Runs three data-quality reports (record audit, string normalisation, missing-value fill) on pipeline duration records containing run_seconds and processed_rows fields, for up to 200 rows.

Runs three local analytical reports on pipeline duration records — a record quality audit, string normalisation, and grouped numeric summary — for up to 200 rows of run_seconds or processed_rows data.

Runs three analytical reports on supplied pipeline duration records: a record quality audit, string normalisation, and IQR-based outlier flagging for run_seconds or processed_rows fields.

Runs three analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on pipeline duration records containing run_seconds and processed_rows fields, for up to 200 rows.

Runs three analytical reports (record quality audit, string normalisation, and numeric distribution) on up to 200 pipeline duration records containing run_seconds and processed_rows fields.

Runs three local analytical reports on pipeline duration records — record quality audit, string normalisation, and numeric row ordering — for up to 200 rows of run_seconds and processed_rows data.

Computes a secondary-measure weighted mean and a date-by-category pivot table for supplied pipeline duration records (run_seconds, processed_rows), returning two analytical reports for up to 200 rows.

Computes a secondary-measure weighted mean and fills missing values for pipeline duration records (run_seconds, processed_rows) across up to 200 rows, returning 2 analytical reports.

Runs two local analytical reports on pipeline duration records: string normalisation and adjacent row change detection, for up to 200 rows of run_seconds or processed_rows data.

Performs string normalisation and category frequency analysis on up to 200 pipeline duration records (run_seconds, processed_rows), returning two independent analytical reports.

Generates string normalisation and equal-width histogram reports for pipeline duration records (run_seconds, processed_rows) from up to 200 supplied rows.

Normalises string fields and produces a grouped numeric summary report for up to 200 pipeline duration records covering run_seconds and processed_rows.

Normalises string fields and flags IQR-based outliers in pipeline duration records (run_seconds, processed_rows), returning two independent analytical reports for up to 200 rows.

Analyzes pipeline duration records (run_seconds, processed_rows) by running string normalization and numeric distribution reports on up to 200 supplied rows.

Computes a trailing mean time series and fills missing values for pipeline duration records (run_seconds, processed_rows) across up to 200 rows.

Computes a Pearson/rank correlation between run_seconds and processed_rows, and performs a keyed dataset comparison between two supplied pipeline duration datasets — both delivered as a single paid response.

Computes Pearson correlation between run_seconds and processed_rows, plus a weighted mean for a chosen secondary measure, across up to 200 pipeline duration records.

Searches for places by text query and returns a list of matching location results

Searches live public web pages for policy comment deadlines specifically relevant to AI agent builders, returning source links and snippets.

Searches live public web pages for government policy comment deadlines relevant to developers and returns source links with snippets.

Searches live public web pages for government policy comment deadlines relevant to technology buyers, returning source links and snippets.

Validates that declared byte counts for policy text excerpts do not exceed per-excerpt and total caller-defined limits, without downloading or processing the actual text.

Compares two sets of closed policy excerpt projections for semantic parity by stable identity, scope, jurisdiction, source pin, and effective time, ignoring row order and natural-language equivalence.

Counts joint absence (co-occurrence of missingness) across policy jurisdiction, scope, and effective-time metadata fields in supplied policy excerpt records.

Compares the order and content digests of policy clauses between two extracts, identifying reorderings and differences in shared clause sequences.

Identifies and maps changes between two pinned policy revisions, surfacing differences in clause identifiers, jurisdiction, scope, and effective-time metadata.

Resolves policy excerpt source and revision declarations against a supplied policy source manifest to determine metadata pin coverage, without evaluating passage support or policy authority.

Submit bug reports, likes, dislikes, or feature requests attributed to a paying wallet address via micropayment.

Counts co-occurring missing values across owner, revision, and content pins in polymer lot/method-pinned measurement records without treating absent values as matching.

Purchases $5 USDC worth of PONG tokens (contract 0x2C2c86E4796fB22ca7169f7cdB4d3a2F9eB08B07) on behalf of the caller via a paid API call

Retrieves the live launch feed for Pons v2 tokens on Robinhood Chain, including full on-chain launch records and token market data

Fetches on-chain liquidity pool analytics from the AmanChain DEX, executed deterministically against live consensus state and paid per call via x402 in USDC.

Computes period-over-period changes in the deviation between actual Bitcoin pool payouts and expected mining rewards across a series of rows.

Calculates the deviation between actual received BTC payouts and expected BTC rewards for up to 200 mining pool entries in a single batch request.

Compares mining pool payout deviation from expected reward across two time periods (before and after), reporting how actual BTC received diverged from expected BTC in each period.

Generates a distribution histogram comparing actual Bitcoin pool payouts to expected rewards across multiple mining rounds

Summarizes the statistical distribution of actual mining pool payouts relative to expected BTC rewards across a batch of records

Detects IQR-based statistical outliers in mining pool payout records by comparing received BTC against expected BTC reward amounts.

Generates a one-input sensitivity table comparing actual pool payout (received_btc) against expected mining reward (expected_btc) across a range of user-supplied values.

Analyzes the ordered trend of actual mining pool payouts against expected BTC rewards across a series of rows, identifying deviation patterns over time.

Computes a rolling mean of the deviation between actual BTC received and expected BTC reward across a series of mining pool payouts.