All Services

Computes local finite-change elasticity of token trading volume relative to market capitalization for a given baseline and step size

Computes and compares token trading volume relative to market capitalization across multiple named scenarios using supplied token observations.

Computes a one-input sensitivity table for the token volume-to-market-cap ratio, varying either volume or market cap across a supplied range of values against a baseline.

Computes a weighted mean of the volume-to-market-cap ratio across multiple token observations using caller-supplied weights.

Acts as a unified pay-per-call gateway aggregating 2,000+ AI-agent tools and x402 services, paid per call in USDC via HTTP 402 with no signup or API key required.

Aggregated pay-per-call gateway that routes AI-agent tool requests across 2,000+ capabilities, paid per call in USDC via x402 with no signup or API key required

Acts as a unified gateway aggregating 2,000+ x402 services and built-in tools, callable per-use in USDC with no signup or API key required.

Looks up geolocation and metadata for a given IPv4 or IPv6 address, payable per-call via x402 in USDC with no account or API key required.

Returns data and analysis about psychological heuristics, paid per-call via x402 with no account required

Returns psychological effects data and analysis for a given query, paid per-call via x402 micropayment with no account required.

Returns data and analysis on psychological biases, with payment handled inline per call via x402 protocol

Downloads a ZIP file binary via a pay-per-call GET request using x402 USDC micropayments on Base, no API key required.

Compares closed tool argument-metadata field contracts to detect request-ingestion directionality breaks between two schema snapshots.

Binds call-context contract declarations to a retained tool-contract manifest and produces a coverage map showing which tools are covered by which call contexts, without verifying actual schemas or implementations.

Evaluates an API endpoint's agent card and assesses its readiness for agent-to-agent (A2A) protocol compatibility

Compares caller-required tool-schema declaration review-check IDs against supplied states to identify missing or unknown condition coverage gaps.

Audits declared tool-schema request/response byte ceilings against caller-specified limits, reporting compliance without executing real traffic.

Compares two sets of parsed tool-schema declaration exports to verify semantic parity, preserving owner, revision, schema pair, and scope labels while ignoring ordering differences.

Counts joint missing owner, input schema, and output schema pins across a batch of tool-schema declarations to quantify co-occurrence patterns of incomplete metadata fields.

Inspects declared tool-schema inheritance links to detect missing nodes, cycles, and cross-owner references without resolving external dependencies.

Compares two revisions of tool-schema declarations and returns a delta of changes to input/output pins, targets, and scope labels as contract metadata.

Maps tool-schema declarations (owner, revision, input/output) to a source manifest, identifying which declared schemas lack source coverage.

Compares declared request and response coordinates in tool-schema declarations to detect unit-label inconsistencies, keeping UTF-8 bytes, codepoints, and tokens distinct without any tokenizer or remote execution.

Marks or features a specific service listing in the ToolVet vetted API/MCP directory

Deduplicates exact duplicate rows and applies numeric ordering to tourism observation records (visitor_count, overnight_stays) for up to 200 rows, returning 2 analytical reports.

Computes numeric distribution statistics and a base-100 index series for supplied tourism records (visitor_count or overnight_stays) across up to 200 rows.

Runs three analytical reports on tourism observation records: a record quality audit, exact row deduplication, and numeric row ordering for visitor_count or overnight_stays fields.

Audits tourism observation records for data quality issues and sorts rows numerically for visitor_count or overnight_stays, returning 2 independent analytical reports for up to 200 rows.

Applies string normalisation and base-100 indexing to supplied tourism observation records (visitor_count or overnight_stays) for up to 200 rows, returning two analytical reports.

Normalises strings and deduplicates exact rows in tourism observation records containing visitor counts and overnight stays, producing 2 analytical reports for up to 200 rows.

Computes a trailing mean series and a secondary-measure weighted mean for up to 200 tourism observation records (visitor_count, overnight_stays) using local analytical engines.

Returns a prepaid M2M session desk pack for high-volume autonomous agent callers, providing session status and desk-level context at 40 calls per 24-hour window.

Retrieves a snapshot of Pearl L1 network telemetry including RPC node status, community node map, and poll history

Analyzes up to 200 trade observation records to produce a duplicate key report and a Pearson/Spearman correlation analysis between export_amount and import_amount.

Computes grouped numeric summaries and numeric row ordering for trade observation records (export_amount, import_amount) across up to 200 rows, returning 2 independent analytical reports.

Audits record quality and applies numeric row ordering to trade observation data (export_amount, import_amount) for up to 200 rows, returning two independent analytical reports.

Runs three local analytical reports on trade observation records: record quality audit, string normalisation, and explicit missing-value fill for export_amount and import_amount fields.

Audits data quality, normalises strings, and filters by numeric interval across up to 200 trade observation rows (export_amount, import_amount), returning 3 analytical reports.

Normalises string fields and produces a date-by-category pivot table for trade observation records (export_amount and import_amount) across up to 200 rows.

Performs string normalisation and secondary-measure weighted mean computation on supplied trade observation records (export_amount, import_amount) for up to 200 rows, returning two analytical reports.

Computes adjacent row-to-row changes and a secondary-measure weighted mean for up to 200 trading journal records containing entry and exit price data.

Computes a Base-100 index series and fills missing values for up to 200 trading journal rows containing entry_price and exit_price fields

Analyzes up to 200 trading journal records to produce two reports: category frequency counts by instrument group and numeric row ordering for entry or exit price fields.

Computes a daily sum series and a secondary-measure weighted mean over trading journal records (entry_price, exit_price) for up to 200 rows, returning two analytical reports.

Analyzes up to 200 trading journal rows to produce a duplicate key report and a weighted mean for a chosen price field (entry_price or exit_price).

Deduplicates and numerically orders up to 200 trading journal rows (entry_price, exit_price), producing 2 analytical reports using local engines.

Filters trading journal rows by a numeric range on entry_price or exit_price and computes a weighted mean of the secondary measure, returning 2 analytical reports for up to 200 rows.

Analyzes trading journal entry/exit price records to produce two reports: numeric row ordering and adjacent row change calculations for up to 200 rows.

Runs three local analytical reports on trading journal rows (entry_price, exit_price): a record quality audit, exact row deduplication, and numeric row ordering — all offline, no external calls.

Audits record quality, normalises strings, and computes adjacent row changes for up to 200 trading journal rows (entry_price or exit_price), returning 3 independent analytical reports.

Audits data quality, normalises strings, and deduplicates rows for trading journal observation records containing entry and exit prices.

Runs three local analytical reports on trading journal rows (entry_price, exit_price): a record quality audit, string normalisation, and grouped numeric summary — no external data or predictions.

Runs three local analytical reports on trading journal rows (entry_price, exit_price): a record quality audit, string normalisation, and a secondary-measure weighted mean — no live data or predictions.

Runs three local analytical reports on trading journal rows (entry_price, exit_price): a record quality audit, string normalisation, and two-measure correlation — no external lookups or predictions.

Computes a secondary-measure weighted mean and fills missing values for trading journal entry/exit price data across up to 200 rows, returning two analytical reports.

Runs string normalisation and date-by-category pivot analysis on up to 200 trading journal observation rows (entry_price, exit_price), returning two local analytical reports.

Analyzes trading journal records by normalizing string fields and producing grouped numeric summaries for entry_price and exit_price across up to 200 rows.

Normalises string fields and orders rows numerically for up to 200 trading journal observation records (entry_price, exit_price), producing 2 analytical reports per call.

Runs two local analytical reports on trading journal rows: string normalisation of instrument/date fields and a trailing moving-average series over entry_price or exit_price.

Computes adjacent row changes and fills missing values for training assessment records (pre_score, post_score) across up to 200 rows, returning two analytical reports.

Computes category frequencies for course_group and fills missing values (zero, mean, or median) in pre_score or post_score for up to 200 training assessment rows, returning two analytical reports.

Compares two keyed datasets of training assessment scores (pre_score/post_score) and fills missing values, producing 2 analytical reports for up to 200 rows.

Sorts training assessment rows numerically and fills missing pre_score or post_score values using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.

Computes numeric row ordering and secondary-measure weighted mean for training assessment records containing pre_score and post_score fields, returning two analytical reports for up to 200 rows.

Runs three analytical reports on training assessment records (pre_score, post_score): record quality audit, exact row deduplication, and category frequencies — up to 200 rows.

Runs three local analytical reports on training assessment records (pre_score, post_score): a record quality audit, string normalisation, and exact row deduplication — up to 200 rows per call.

Analyses training assessment records (pre_score, post_score) to produce string normalisation and duplicate key reports for up to 200 rows.

Normalises string fields and filters training assessment rows (pre_score, post_score) by a numeric interval, returning two analytical reports for up to 200 rows.

Normalises string fields and computes a secondary-measure weighted mean for up to 200 training assessment rows containing pre_score and post_score values, returning two analytical reports per call.

Sorts up to 200 training attendance records numerically by a chosen field (attendees or completions) and returns the ordered rows.

Analyzes training compliance data (completed_courses, assigned_courses) to detect adjacent row changes and fill missing values using zero, mean, or median imputation — two reports for up to 200 rows.

Runs two local analytical reports on training compliance data: a duplicate key report and a date-by-category pivot, for up to 200 rows of completed/assigned course records.

Runs two local analytical reports on training compliance data: a duplicate key report and a numeric interval filter for completed_courses or assigned_courses fields, across up to 200 rows.

Analyzes training compliance records to produce a duplicate key report and numeric row ordering for up to 200 rows of completed/assigned course data.

Analyzes up to 200 training compliance records to produce a duplicate key report and a trailing mean series for completed or assigned courses.

Deduplicates training compliance rows and generates a Base-100 indexed series for completed_courses or assigned_courses across up to 200 records.

Deduplicates training compliance records and produces a grouped numeric summary of completed and assigned courses, for up to 200 rows in a single call.

Deduplicates training compliance records and computes a secondary-measure weighted mean for completed_courses and assigned_courses data, producing two analytical reports.

Computes a numeric distribution report and a base-100 indexed time series for supplied training compliance data (completed_courses, assigned_courses) across up to 200 rows.

Applies a numeric interval filter and keyed dataset comparison to submitted training compliance records (completed_courses, assigned_courses) for up to 200 rows across two reports.

Generates two analytical reports on training compliance data: numeric row ordering and a date-by-category pivot table, for up to 200 supplied rows.

Sorts training compliance records numerically by row and fills missing completed_courses or assigned_courses values using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.

Computes numeric row ordering and a trailing mean series for up to 200 training compliance records (completed_courses, assigned_courses) in two independent local analytical reports.

Audits training compliance records for data quality issues and identifies exact duplicate rows across up to 200 supplied rows, returning two independent analytical reports.

Audits training compliance records for quality issues, removes exact duplicate rows, and identifies duplicate keys across completed_courses and assigned_courses data — three reports for up to 200 rows.

Runs three local analytical reports on training compliance row data: a record quality audit, exact row deduplication check, and numeric row ordering for completed_courses or assigned_courses fields, supporting up to 200 rows per call.

Runs three local analytical reports on training compliance data: a record quality audit, string normalisation, and an equal-width histogram — all on up to 200 rows of course completion data.

Runs three independent analytical reports on training compliance records (completed_courses, assigned_courses): record quality audit, string normalisation, and keyed dataset comparison for up to 200 rows.

Runs three analytical reports on training compliance data (completed_courses, assigned_courses): record quality audit, string normalisation, and correlation between the two measures, for up to 200 rows.

Computes a secondary-measure weighted mean and fills missing values for training compliance records (completed_courses, assigned_courses) across up to 200 rows, returning 2 analytical reports per call.

Normalises string fields and computes category-frequency distributions across up to 200 training compliance records (completed_courses, assigned_courses).

Normalises string fields and fills missing values in training compliance records (completed_courses, assigned_courses) for up to 200 rows, producing 2 analytical reports per call.

Normalises team/group strings and computes a secondary-measure weighted mean for up to 200 training compliance records (completed_courses, assigned_courses) across two independent analytical reports.

Computes a two-measure correlation and a secondary-measure weighted mean for training compliance records (completed_courses, assigned_courses) across up to 200 rows, returning two statistical reports.

Validates date columns in a CSV dataset (up to 1000 rows) against configurable min/max bounds and allowed category values, returning a calendar date check report.

Validates cell lengths in a CSV training dataset, checking each cell against configurable min/max thresholds and allowed category values, up to 1000 rows.

Validates a declared CSV table contract (headers, types, categories, numeric ranges, cell lengths) for training example metadata, returning a structured report for up to 1000 rows.

Validates a CSV file's whitespace formatting and declared table contract, checking up to 1000 rows for whitespace issues in cells, headers, and values.

Scans a training lesson document (Markdown or plain text) for duplicate paragraphs and returns a structured audit report.

Analyzes the length and paragraph structure of a training lesson document (Markdown or plain text) and returns a deterministic structural profile.