All Services

Checks whether a given string is a valid IPv4 address

Calculates the ratio of uppercase letters to total characters in a given text string

Performs date arithmetic and calendar computations given date-related query parameters

Calculates the keyword density of text content, returning frequency and distribution metrics for keywords

Computes the R-squared (coefficient of determination) statistical measure for a given dataset

Returns the count of significant digits (significant figures) in a given numeric input

Analyzes and validates security properties of a blockchain token, returning security risk assessments and metadata.

Parses and validates a cryptocurrency or postal address string, extracting its structured components and type information.

Computes or formats a number to the correct number of significant figures

Converts or processes content into Markdown format via a paid x402 micropayment endpoint

Converts a land area measurement from hectares to acres via a simple GET request

Converts a raw byte count into a human-readable file size string (e.g. 1024 → '1 KB')

Validates XML content by checking its structure, type, and associated parameters for correctness

Fetches and extracts plain text content from a web URL via a pay-per-use x402-gated GET endpoint

Returns the current time (and likely timezone/UTC details) via a paid x402 HTTP endpoint

Returns intelligence and metadata about a crypto wallet address, including balance, activity, and risk signals.

Decodes and parses x402 payment protocol transaction data, returning structured information about transaction type, method, and parameters.

Returns the capital city of a given country or region via a simple GET request.

Detects the natural language of a given text input and returns the identified language.

Performs mathematical computations or calculations via a paid x402 micro-payment API endpoint

Returns foreign exchange (forex) rate data for currency pairs via a pay-per-call x402 API

Converts plain text input into structured JSON output using AI parsing

Converts an integer to its Gray code (reflected binary code) representation

Returns abundant numbers (numbers whose proper divisors sum to more than the number itself) for a given input via a paid API endpoint.

Computes the cash conversion cycle (CCC) financial metric from input parameters, measuring how long a company takes to convert investments into cash flows from sales

Returns a registry of blockchain chains supported by the x402 payment protocol

Returns current blockchain gas price data for use in transaction cost estimation and planning

Given a number, finds its amicable pair — the other number in an amicable pair where each number equals the sum of the proper divisors of the other.

Returns the portion of a string that appears before a specified delimiter substring

Computes the next optimal move for a 2048 puzzle board state

Scrapes and extracts plain text content from a web page or URL via a paid GET request

Checks whether a given string or number represents a valid octal (base-8) value

Generates a universally unique identifier (UUID) via a paid x402 HTTP endpoint

Checks whether a given date or time is in the past

Encodes or decodes data using Base64 encoding via a paid x402 API endpoint

Scans a smart contract or x402 endpoint to assess its security, payment verification, and compliance characteristics

Computes the sum of an arithmetic series given its parameters

Returns current cryptocurrency price data via a pay-per-call x402 micropayment endpoint.

Performs DNS lookups for a given domain or hostname, returning DNS record data

Provides premium security scanning, auditing, and intelligence capabilities for domains, contracts, or systems.

Sorts an array of elements using the quicksort algorithm, accessible as a paid API endpoint via the x402 micropayment protocol.

Converts raw numeric values into human-readable, formatted strings (e.g. 1000000 → '1 million')

Generates a concise AI-focused news briefing or summary report on demand

Performs a web search query and returns results, accessible via the x402 micropayment protocol

Performs a site audit on a given web resource, returning structured analysis of the site's quality, issues, or metadata.

Parses a User-Agent string or request metadata to extract structured browser, OS, and device information

Retrieves intelligence and metadata about a domain, such as registration, ownership, DNS, or threat/reputation signals.

Checks the safety and risk profile of a token swap before execution on a DEX

Converts a human-readable date string to a Unix timestamp integer

Encodes Ethereum ABI data for smart contract interactions given type, method, and parameter specifications

Determines whether numbers are abundant (where the sum of proper divisors exceeds the number itself)

Performs a breadth-first search (BFS) graph traversal computation as a paid API service.

Sends a text prompt to a MiniMax-M3 428B MoE model hosted via NVIDIA NIM and returns an AI-generated response, payable per-call in USDC on Base.

Analyzes the sentiment of a given text using NVIDIA NIM's MiniMax-M3 (428B MoE) model, returning a sentiment classification.

Computes a distribution histogram of replenishment gaps (minimum stock minus available plus inbound units) across up to 200 retail inventory records, grouped into configurable bins.

Computes a statistical distribution summary (quartiles, min, max, mean, etc.) for the replenishment gap between minimum required stock and available+inbound units across up to 200 retail records.

Detects IQR-based outlier records in minimum-stock replenishment gap calculations for up to 200 retail inventory rows.

Compares named override scenarios against a baseline to reveal how minimum-stock replenishment gaps shift under different inventory assumptions.

Generates a one-input sensitivity table showing how the minimum-stock replenishment gap changes as a single inventory variable (minimum units, available units, or inbound units) varies across supplied values

Ranks up to 200 retail inventory records by their minimum-stock replenishment gap, identifying which items are most critically understocked relative to minimums and inbound supply.

Computes a rolling mean of the replenishment gap (minimum stock minus available stock minus inbound units) across up to 200 retail inventory records

Computes a sequential (waterfall) attribution of how changes in available, inbound, and minimum stock units contributed to a shift in the replenishment gap for retail inventory.

Calculates the target replenishment gap for minimum-stock retail inventory by comparing available, inbound, and minimum units against a supplied target value.

Generates a two-input scenario grid showing how the minimum-stock replenishment gap changes across combinations of minimum_units, available_units, and inbound_units values for retail inventory.

Calculates the weighted mean of the replenishment gap (units needed to meet minimum stock levels) across multiple retail inventory rows.

Computes adjacent period-over-period changes in mining availability fraction (uptime ratio) given running hours and scheduled hours for each period.

Calculates the mining availability fraction (uptime ratio) for up to 200 equipment records in a single batch, given running hours and scheduled hours per row.

Computes and compares mining availability fraction (running hours ÷ scheduled hours) for two time periods, returning a side-by-side before/after report.

Computes the Pearson/rank correlation between a series of mining availability fractions (running hours / scheduled hours) and a corresponding benchmark series.

Computes a bounded distribution histogram of mining availability fractions from supplied running-hours and scheduled-hours row data.

Computes a statistical distribution summary for mining availability fraction (uptime ratio) given arrays of running hours and scheduled hours data.

Detects IQR-based outliers in mining equipment availability fractions computed from running hours and scheduled hours data.

Computes and compares named scenarios of mining equipment availability fraction (running hours / scheduled hours) against a baseline, returning a bounded report for up to 20 labeled scenarios.

Generates a one-input sensitivity table for mining availability fraction by varying either running hours or scheduled hours across user-supplied values against a baseline configuration.

Ranks a set of mining equipment records by their availability fraction (running hours divided by scheduled hours) using a bounded, formula-based computation.

Computes a rolling mean of mining availability fraction (running hours / scheduled hours) over a sliding window across up to 200 input rows.

Runs a sequential waterfall attribution analysis showing how each input change (running hours, scheduled hours) contributes to the delta in mining availability fraction.

Calculates the mining availability fraction (uptime ratio) from running hours and scheduled hours in a single bounded report.

Classifies whether a mining operation's availability fraction (running hours / scheduled hours) falls within a specified lower/upper threshold range across multiple time periods.

Generates a two-dimensional scenario grid showing how mining availability fraction varies across combinations of running_hours and scheduled_hours values.

Calculates the adjacent period-over-period changes in the break-even Bitcoin price required to cover mining costs given total USD cost and net BTC mined inputs.

Computes a base-100 rebased index of the BTC break-even mining price across multiple cost/yield scenarios, using user-supplied mining assumptions.

Calculates the break-even Bitcoin price for multiple mining scenarios in a single batch, given total cost and net BTC mined per row.

Calculates and compares the break-even Bitcoin price needed to cover mining costs for two different operational scenarios (before and after)

Computes a benchmark correlation report for mining break-even BTC prices given arrays of total costs and net BTC mined, compared against a set of benchmark values.

Computes a distribution summary of break-even Bitcoin prices given supplied mining cost and net BTC yield data rows

Identifies IQR-based outlier rows in a batch of mining break-even BTC price calculations given total mining cost and net BTC mined.

Computes local finite-change elasticity of the Bitcoin mining break-even price with respect to a specified input variable, given user-supplied mining cost and BTC yield assumptions.

Performs an ordered trend analysis on a series of mining cost scenarios to determine the break-even BTC price for each, revealing directional patterns across inputs.

Ranks a batch of mining scenarios by their break-even BTC price, computed from total cost (USD) and net BTC mined, using a fixed formula with disclosed units.

Computes a rolling mean of the break-even BTC price given mining cost and net BTC yield inputs over a specified window size.

Computes how the break-even BTC price for a mining operation shifts as input assumptions change sequentially, attributing the delta to each parameter step.

Classifies a computed mining break-even BTC price as within or outside a user-defined threshold range, given total mining cost and net BTC earned.

Rebases a series of mining break-even energy tariff values to a base-100 index anchored at a chosen row, using supplied revenue, non-energy cost, and energy consumption inputs.

Computes the correlation between calculated mining break-even energy tariffs and a set of benchmark values across multiple supplied data rows.

Computes a distribution histogram of break-even energy tariff values across up to 200 supplied mining operation rows, using provided revenue and non-energy cost assumptions.

Computes a statistical distribution summary of the break-even energy tariff for cryptocurrency mining operations given revenue, non-energy costs, and energy consumption inputs.

Computes the local finite-change elasticity of the mining break-even energy tariff with respect to a specified input variable, given baseline mining assumptions.

Compares multiple named mining scenarios against a baseline to compute break-even energy tariff rates, given revenue and non-energy cost assumptions.

Computes ordered trend analysis of the break-even energy tariff for cryptocurrency mining across multiple scenario rows using supplied revenue and cost assumptions.