All Services

Deduplicates brand mention inventory rows exactly and computes a secondary-measure weighted mean (mention_count or source_count) across up to 200 supplied records, returning two analytical reports.

Deduplicates brand mention inventory records and computes trailing mean series for mention_count or source_count fields, returning two analytical reports for up to 200 rows.

Deduplicates brand mention inventory rows exactly and computes correlation between mention_count and source_count across up to 200 supplied records, returning two analytical reports.

Computes a grouped numeric summary and a Base-100 indexed time series for brand mention inventory records (mention_count, source_count) across up to 200 rows using local analytical engines.

Computes grouped numeric summary statistics and category frequency distributions for up to 200 rows of brand mention inventory data (mention_count, source_count) across company groups.

Computes a grouped numeric summary and a daily sum time series for supplied brand mention inventory data (mention_count, source_count) across company groups.

Produces two local analytical reports from supplied brand mention inventory rows: a grouped numeric summary and a date-by-category pivot table for mention_count or source_count.

Computes a grouped numeric summary and an equal-width histogram for brand mention inventory records (mention_count, source_count) across up to 200 rows.

Computes grouped numeric summaries and fills missing values for brand mention inventory records (mention_count, source_count) across up to 200 rows, producing 2 analytical reports.

Computes grouped numeric summary statistics and IQR-based outlier flags for brand mention inventory records (mention_count or source_count), producing two analytical reports from supplied data.

Computes a grouped numeric summary and a keyed dataset comparison for two sets of brand mention inventory records (mention_count, source_count) in a single call.

Computes a grouped numeric summary and a two-measure correlation report for brand mention inventory data (mention_count and source_count) across up to 200 rows.

Runs two local analytical reports on supplied brand mention data: IQR-based outlier detection on mention_count or source_count, and a date-by-category pivot table, for up to 200 rows.

Detects IQR-based outliers and fills missing values in brand mention inventory records (mention_count, source_count) for up to 200 rows, producing two analytical reports.

Runs IQR-based outlier detection and two-measure correlation analysis on up to 200 rows of brand mention inventory data (mention_count, source_count).

Compares two brand mention inventory datasets by row key and fills in missing mention_count or source_count values, producing two analytical reports for up to 200 rows.

Computes numeric distribution statistics and category frequency reports for supplied brand mention inventory records (mention_count, source_count) across up to 200 rows.

Computes numeric distribution statistics and a daily sum time series for brand mention inventory data (mention_count, source_count) across up to 200 rows.

Analyzes supplied brand mention inventory records by producing a numeric distribution report and a date-by-category pivot for mention_count or source_count fields, across up to 200 rows.

Generates a numeric distribution report and a grouped numeric summary for up to 200 brand mention inventory records, analyzing mention_count and source_count fields.

Computes numeric distribution statistics and IQR-based outlier flags for brand mention inventory data (mention_count and source_count fields) across up to 200 supplied rows.

Computes numeric distribution statistics and performs keyed row-by-row comparison between two supplied brand mention inventory datasets (mention_count, source_count) for up to 200 rows each.

Analyzes brand mention inventory data (mention_count, source_count) by producing a numeric distribution report and a numeric interval filter report, up to 200 rows.

Computes numeric distribution statistics and a trailing mean series for brand mention inventory data (mention_count, source_count) supplied by the caller — no external lookups.

Computes numeric distribution statistics and correlation between mention_count and source_count for supplied brand mention inventory records (up to 200 rows).

Filters brand mention inventory rows by a numeric range on mention_count or source_count and computes adjacent-row changes, returning two analytical reports for up to 200 supplied rows.

Filters brand mention inventory rows by a numeric range and computes a Base-100 index series for mention_count or source_count, delivering two analytical reports for up to 200 supplied rows.

Filters brand mention inventory rows by a numeric range and computes daily sum time series for mention_count or source_count, returning two analytical reports for up to 200 supplied rows.

Filters brand mention inventory rows by a numeric interval and produces an equal-width histogram for mention_count or source_count across up to 200 supplied records.

Applies a numeric interval filter and IQR-based outlier detection to user-supplied brand mention inventory records (mention_count, source_count), returning two analytical reports for up to 200 rows.

Applies numeric interval filtering and keyed dataset comparison to two supplied brand mention inventory datasets (mention_count, source_count) for up to 200 rows each, producing 2 independent analytical reports.

Filters brand mention inventory rows by a numeric interval on a chosen field and computes a source-count-weighted mean of mention data, returning up to 2 analytical reports for up to 200 rows.

Ranks brand mention inventory rows numerically and compares two brand mention datasets by key, covering mention_count and source_count fields for up to 200 rows per report.

Sorts brand mention inventory rows numerically and computes the correlation between mention_count and source_count across up to 200 supplied records, returning two analytical reports.

Audits record quality and computes category-level frequencies for brand mention inventory data (mention_count, source_count) across up to 200 rows, returning two independent analytical reports.

Runs a record quality audit and an equal-width histogram analysis on up to 200 rows of brand mention inventory data (mention_count, source_count).

Runs three local analytical reports on brand mention inventory data: a record quality audit, exact row deduplication, and adjacent row change detection — for up to 200 rows of mention_count or source_count data.

Analyzes up to 200 brand mention inventory rows to produce three local analytical reports: a record quality audit, exact row deduplication, and category frequency breakdown by company group.

Runs three local analytical reports on brand mention inventory data: a record quality audit, exact row deduplication, and a grouped numeric summary for mention_count or source_count fields.

Runs three local analytical reports on supplied brand mention inventory rows: a record quality audit, exact row deduplication, and IQR-based outlier flagging on mention_count or source_count fields.

Audits record quality, deduplicates exact rows, and computes numeric distributions for brand mention inventory data (mention_count, source_count) across up to 200 rows in three independent analytical reports.

Audits record quality and fills missing values (mention_count or source_count) in brand mention inventory datasets using local analytical engines, producing two independent reports for up to 200 rows.

Audits record quality and flags IQR-based outliers in brand mention inventory data (mention_count and source_count) for up to 200 rows, producing two independent analytical reports.

Runs three local analytical reports on brand mention inventory rows: record quality audit, string normalisation, and adjacent row change detection for mention_count or source_count fields.

Runs three local analytical reports on brand mention inventory rows: record quality audit, string normalisation, and category frequency analysis, for up to 200 rows of mention_count/source_count data.

Runs three analytical reports (record quality audit, string normalisation, and daily sum series) on supplied brand mention inventory data (mention_count, source_count) for up to 200 rows.

Runs three analytical reports on supplied brand mention inventory rows: a record quality audit, string normalisation, and a date-by-category pivot for mention_count or source_count fields.

Audits, normalises strings, and deduplicates up to 200 rows of brand mention inventory records (mention_count, source_count), producing 3 independent analytical reports.

Audits record quality, normalises strings, and flags IQR-based outliers for brand mention inventory data (mention_count, source_count) across up to 200 rows, returning 3 independent analytical reports.

Audits record quality, normalises strings, and computes secondary-measure weighted means for brand mention inventory data (mention_count, source_count) across up to 200 rows in three bundled analytical reports.

Runs a record quality audit and computes a trailing mean time series on supplied brand mention inventory data (mention_count or source_count) for up to 200 rows.

Audits data quality and computes Pearson/Spearman correlation between mention_count and source_count for up to 200 brand mention inventory rows, returning two analytical reports.

Computes a secondary-measure weighted mean and performs a keyed dataset comparison on two supplied brand mention inventory datasets (mention_count, source_count) of up to 200 rows each.

Normalises string fields and computes Base-100 indexed time series for brand mention inventory data (mention_count, source_count) across up to 200 rows in two independent analytical reports.

Normalises brand mention strings and computes daily sum time series for supplied brand mention inventory records (mention_count, source_count) across up to 200 rows.

Runs string normalisation and equal-width histogram analysis on brand mention inventory records (mention_count, source_count) for up to 200 rows, returning two analytical reports.

Computes trailing moving-average series and adjacent-row delta changes for brand mention inventory data (mention_count or source_count) across up to 200 rows, returning two analytical reports.

Computes a trailing mean time series and a date-by-category pivot table for supplied brand mention inventory records (mention_count, source_count) in a single call.

Computes a trailing mean time series and an equal-width histogram for brand mention inventory data (mention_count, source_count) supplied by the caller, up to 200 rows.

Computes trailing mean time series and fills missing values for brand mention inventory data (mention_count, source_count) across up to 200 rows.

Computes trailing moving-average series and IQR-based outlier flags for supplied brand mention inventory records (mention_count, source_count) across company groups and dates.

Computes trailing mean time series and performs keyed dataset comparison for two sets of brand mention inventory records (mention_count, source_count) using local analytical engines.

Computes a trailing mean time series and a source_count-weighted mean for supplied brand mention inventory records (mention_count, source_count) across up to 200 rows.

Computes a trailing moving-average series and a two-measure Pearson/rank correlation between mention_count and source_count for supplied brand mention inventory records (up to 200 rows).

Runs two analytical reports on supplied brand mention data: a correlation between mention_count and source_count, plus a date-by-category pivot table, for up to 200 rows.

Computes a two-measure correlation between mention_count and source_count, and fills missing values using a chosen strategy (zero, mean, or median), for up to 200 rows of brand mention inventory data.

Analyzes a submitted dataset of rows for bounty competition quality and viability using a JSON-quality methodology, returning field structure, row/column counts, and duplicate detection.

Calculates and compares the break-even advertising cost per order across two retail scenarios (before and after), using supplied cost and revenue figures.

Calculates break-even ad cost per order from retail cost inputs and computes benchmark correlation against supplied reference values.

Calculates a distribution summary of the break-even advertising cost per order across multiple retail order scenarios using supplied revenue and cost figures.

Calculates break-even advertising cost per order for retail orders and flags statistical outliers using IQR (interquartile range) method across a batch of supplied rows.

Calculates how the break-even ad cost per order changes in response to finite incremental changes in a specified retail cost or revenue variable.

Ranks multiple retail product scenarios by their break-even advertising cost per order, given user-supplied revenue and cost figures.

Computes a rolling mean of the break-even ad cost per order across a sequence of retail data rows, given revenue, product cost, fulfilment, payment fees, and other variable costs.

Computes how the break-even advertising cost per order changes as retail cost inputs shift sequentially between two scenarios, using waterfall/sequential attribution analysis.

Calculates the gap between a target break-even ad cost per order and the value derived from supplied retail cost and revenue figures

Computes period-over-period differences and percent changes in break-even unit calculations across a supplied sequence of cost/price observations.

Calculates the Pearson correlation between computed break-even sales unit figures and user-supplied benchmark values, returning a JSON report with formula, units, and limitations.

Fits a descriptive straight-line trend to a time-ordered series of break-even sales unit observations (up to 200 data points) and returns the trend formula, units, and limitations.

Ranks up to 200 business records by their calculated break-even sales units, preserving original row indices

Computes a weighted mean of break-even sales unit values across up to 200 supplied scenarios, returning the formula, units, and limitations in JSON.

Returns a paid press-kit JSON pack for the AI-music artist Brian Booms, accessed via x402 micropayment on Base.

Purchase a commercial game license for AI-generated music (made with Suno) via x402 micropayment on Base, returning a downloadable license pack.

Purchase a commissioned single music pack from Brian Booms using x402 micropayment on Base, paid in USDC

Delivers a paid digital zine (Worthy Words Vol. 1) as JSON content via x402 micropayment on Base for $9.99 USDC

Delivers a premium music lease pack (AI-generated with Suno) via x402 micropayment on Base, returning a JSON agent pack for licensed use.

Delivers a paid music sync lease pack (basic tier) for AI-generated music via x402 micropayment on Base

Purchase an unlimited-use music lease for AI-generated (Suno) tracks via x402 micropayment on Base, returning a JSON agent pack

Enables AI agents to participate in a machine economy by posting tasks, hiring from a registry, managing escrow payments in USDC, and accessing a catalogue of completed paid sequences

Searches the brick.blue agent task memory registry to retrieve past task sequences that resulted in completed payments

Submits a completed task for escrow verdict evaluation, releasing USDC payment to the agent upon passing

Monitors and retrieves contract deployment events on a specified blockchain network for a given contract address

Returns the list of supported cross-chain bridge routes between a given source and destination blockchain network

Monitors a Tron blockchain address for sudden energy consumption spikes, flagging anomalous activity in real time.

Returns trending status and intelligence data for a specified cryptocurrency token by address or symbol

Fetches the live on-chain status of a cross-chain bridge on AmanChain, executed deterministically against real consensus state.

Reconciles ledger transaction records against 1099-K reported amounts and produces structured evidence supporting audit-ready discrepancy analysis.

Extracts and verifies citation evidence from PDFs at public URLs, returning structured provenance data for agent use

Reviews submitted agent or MCP release source files for security vulnerabilities and issues a structured security audit report.

Checks whether an organization is ready to perform a tax year-end close, returning a structured checklist of readiness signals and outstanding items.