All Services

Computes a weighted mean of token circulating market capitalization across multiple price/supply observations, returning a single aggregated value.

Calculates period-over-period changes in the circulating supply fraction (circulating/total tokens) for a sequence of token observations.

Computes a Base-100 rebase index series for token circulating supply fraction (circulating/total tokens) across a sequence of observations, anchored to a chosen base row.

Computes and compares the circulating supply fraction of a token between two points in time, returning a deterministic before/after report.

Computes the correlation between a token's circulating supply fraction (circulating/total) and a provided benchmark series over matched observations.

Computes a statistical distribution summary of token circulating supply fraction (circulating/total tokens) across a series of observations.

Calculates IQR-based outlier flags for token circulating supply fraction (circulating_tokens / total_tokens) across a batch of token observations.

Calculates the local finite-change elasticity of a token's circulating supply fraction given baseline token supply figures and a step size.

Compares token circulating supply fraction across multiple named scenarios relative to a baseline, returning a deterministic report for each labeled scenario.

Computes a one-input sensitivity table for the token circulating supply fraction metric, varying either circulating_tokens or total_tokens across a user-supplied value range against a baseline scenario.

Analyzes the ordered trend of token circulating supply fraction (circulating/total tokens ratio) across a time series of observations, returning a deterministic trend report.

Calculates a rolling mean of the circulating supply fraction (circulating tokens / total tokens) over a sliding window across a series of token observations.

Computes a grid of token circulating supply fraction values across two independently varied token quantity axes, enabling scenario analysis over combinations of circulating and total token counts.

Computes a weighted arithmetic mean of the circulating-supply fraction (circulating/total tokens) across multiple token observations.

Calculates period-over-period changes in the ratio of a token's market cap (circulating value) to its fully diluted valuation (FDV) across a supplied time series of observations.

Calculates a Base-100 rebased index series for token market cap vs. fully diluted valuation (FDV), normalizing observations relative to a chosen baseline row

Computes and compares a token's circulating market cap to fully diluted valuation (FDV) ratio across two time snapshots, returning a deterministic before-and-after report.

Computes a statistical distribution summary of the ratio between a token's market cap and its fully diluted valuation (FDV) across a supplied set of observations.

Applies IQR (interquartile range) statistical analysis to a batch of token market-cap vs. fully-diluted-value (FDV) observations to flag outliers.

Computes and compares a token's market-cap-to-FDV ratio across multiple named scenarios, returning a deterministic verdict for each supplied configuration.

Generates a one-input sensitivity table showing how the ratio of a token's market cap to its fully diluted valuation (FDV) changes as either market cap or FDV varies across a supplied range of values.

Analyzes the ordered trend of a token's market cap to fully diluted valuation (FDV) ratio across a time series of supplied observations

Computes a rolling mean of the ratio between a token's circulating market cap and its fully diluted valuation (FDV) over a specified window of observations.

Generates a two-input scenario grid comparing token market cap (circulating) against fully diluted valuation (FDV) across user-specified value ranges

Computes a weighted mean of the ratio of circulating market cap to fully diluted valuation (FDV) across multiple token observations.

Calculates adjacent period-over-period changes in token drawdown from a supplied peak price across a sequence of price observations.

Calculates a base-100 rebased drawdown index for a series of token price observations relative to their supplied peak prices.

Computes and compares token drawdown percentages from supplied peak prices across two time snapshots (before and after), returning a side-by-side report.

Computes the drawdown percentage for each token observation (from supplied peak price) and correlates the resulting drawdown series against a set of benchmark values.

Computes a binned distribution histogram of drawdown percentages from supplied peak prices across a set of token price observations.

Computes a distribution summary of drawdown percentages from supplied peak prices across a set of token price observations.

Computes and compares token price drawdown percentages across multiple named scenarios using caller-supplied peak and current price observations.

Computes a one-input sensitivity table showing token price drawdown percentages from a supplied peak price across a range of input values (peak or current price).

Calculates ordered trend analysis of token drawdown percentages from supplied peak prices across a sequence of observations.

Calculates and ranks drawdown percentages for a list of token price observations relative to their supplied peak prices, returning a deterministic record-ranking report.

Calculates a rolling mean of token price drawdown from supplied peak prices over a configurable window, returning a statistical report for each observation set.

Computes a sequential input-change attribution report for token price drawdown from a user-supplied peak price, comparing before and after scenarios deterministically.

Calculates the percentage drawdown of a token's current price from a supplied peak price in a single deterministic computation.

Calculates the target gap for token drawdown from a supplied peak price, given current and peak token prices.

Classifies whether a token's percentage drawdown from its supplied peak price falls within a caller-defined threshold range, using deterministic computation on provided price observations.

Generates a two-input scenario grid computing token drawdown percentages from a supplied peak price across combinations of peak_price and current_price values.

Computes a weighted mean of token drawdown percentages across multiple peak/current price observation pairs.

Batch-calculates the execution fee as a percentage of notional value for up to 200 token trade observations in a single call.

Compares token execution fee as a percentage of notional value between two scenarios (before and after), reporting the change in fee burden.

Computes the correlation between token execution fee-to-notional ratios and a set of user-supplied benchmark values across up to 200 observations.

Computes a distribution histogram of token execution fee-to-notional ratios from a supplied set of trade observations, returning bucketed frequency analysis across a configurable number of bins.

Computes a statistical distribution summary of token execution fee-to-notional ratios across a batch of supplied trade observations.

Computes a one-input sensitivity table showing how token execution fee relative to notional changes as a single input variable varies across supplied values.

Performs an ordered trend analysis on token execution fees relative to notional trade value, identifying directional patterns in fee burden across a sequence of supplied observations.

Ranks token trade observations by their fee-to-notional ratio (fee burden) across a supplied batch of up to 200 rows, using a deterministic calculation engine.

Computes a rolling mean of token execution fee-to-notional ratios over a sliding window across a sequence of trade observations.

Computes a sequential (waterfall) attribution of how changes in fee_usd and notional_usd individually drive the change in a token's execution fee burden (fee relative to notional).

Classifies whether the execution fee relative to notional trade value falls within caller-defined lower and upper thresholds for a batch of token observations.

Generates a two-dimensional scenario grid showing how token execution fee burden (fee relative to notional) varies across combinations of fee_usd and notional_usd values.

Computes the notional-weighted mean of token execution fee ratios (fee/notional) across a set of trade observations using caller-supplied weights.

Computes a base-100 rebased index series for token fully diluted value (FDV) from a supplied array of price and fully-diluted-token-supply observations.

Computes the statistical correlation between a token's fully diluted value (FDV) time-series observations and a set of benchmark values, returning a deterministic correlation report.

Computes a statistical distribution summary of token fully diluted value (FDV) from a set of price and total-supply observations.

Computes the local finite-change elasticity of a token's fully diluted value (FDV) with respect to a specified field (price or supply), given a baseline and a step size.

Analyzes a series of token price and fully-diluted-supply observations to compute an ordered trend report for the token's fully diluted valuation (FDV).

Computes a sequential (waterfall) attribution of how changes in token price and total supply each contribute to a change in a token's fully diluted value (FDV).

Calculates the gap between a token's current fully diluted valuation (FDV) and a specified target FDV, given token price and total supply.

Classifies a token's fully diluted valuation (FDV) as within or outside caller-defined lower/upper thresholds, based on supplied price and total-supply observations.

Converts a specified amount from USD to multiple foreign currencies using live ECB exchange rates

Computes correlation metrics between crypto assets or DeFi data series for on-chain intelligence and market analysis

Fetches recent Mastodon posts from a given instance, returning post content, author, and engagement data

Answers natural language questions about crypto, DeFi, ERC-20 tokens, OFAC sanctions, and on-chain data by searching the live web and synthesizing results with citations.

Searches Hacker News for posts and discussions matching a given query, returning titles, URLs, points, and comment counts

Retrieves on-chain funding history and capital flow data for crypto/DeFi tokens or protocols

Searches crypto and DeFi topics returning relevant web results with summaries and snippets

Fetches top Lemmy community posts related to a crypto/DeFi topic, returning scored posts with titles, URLs, and comment counts from a specified Lemmy instance

Fetches and converts a web page URL into structured markdown content with title and word count for AI agents

Retrieves data about cryptocurrency derivatives exchanges including trading volumes, open interest, and market analytics

Retrieves DeFi borrowing data and analytics for on-chain lending protocols via the TokenGuard crypto intelligence API

Provides AI agents with crypto/DeFi intelligence including ERC-20 token safety analysis, market data, DeFi analytics, and on-chain intelligence via a micropayment-gated bridge endpoint.

Retrieves SEC EDGAR financial concept data (e.g. revenues, assets) for public companies by CIK, returning structured filing series across fiscal years

Fetches live derivatives market ticker data (funding rates, open interest, mark prices) for crypto assets via the TokenGuard DeFi analytics API

Provides access to a 65-route crypto and DeFi data API covering ERC-20 token safety, market data, DeFi analytics, and on-chain intelligence via a Docker development endpoint with x402 micropayments.

Retrieves aggregated decentralized exchange (DEX) market data including prices, liquidity, and trading analytics across multiple DEX protocols

Returns cryptocurrency market dominance data showing the relative market share of major cryptocurrencies across the total crypto market

Returns historical gas price data for blockchain networks, useful for DeFi analytics and transaction cost analysis

Returns DeFi lending protocol analytics and on-chain data for AI agents, covering rates, positions, and market conditions across lending platforms.

Fetches Morpho protocol on-chain lending/borrowing analytics and DeFi data for AI agents via micropayment-gated API

Returns on-chain NFT collection statistics including floor price, volume, holder counts, and market metrics for a given NFT collection.

Provides on-demand crypto and DeFi intelligence including ERC-20 token safety checks, market data, DeFi analytics, and on-chain data for AI agents via micropayment-gated POST endpoint.

Retrieves on-chain crypto options trading volume data for DeFi analytics and market intelligence

Retrieves protocol fee data for DeFi protocols, enabling agents to analyze fee structures and revenue across on-chain platforms

Returns protocol revenue metrics and analytics for DeFi protocols on-chain

Fetches real-time cryptocurrency price data from the Pyth Network oracle via the TokenGuard DeFi data API

Checks whether a stablecoin is maintaining its price peg and returns peg deviation analytics

Returns current stock prices and market index levels for major equities and indices like S&P 500

Returns a ranked list of trending NFT collections by current market activity and momentum

Fetches and parses an RSS or Atom web feed from a given URL, returning structured items with titles, links, summaries, and publish dates

Retrieves on-chain whale wallet activity, large-holder movements, and significant transaction data for crypto/DeFi intelligence

Fetches cryptocurrency market data from CoinPaprika via the TokenGuard API for AI agents

Returns open interest data for crypto derivatives markets, enabling agents to gauge market positioning and sentiment for specific assets.

Fetches Pendle Protocol DeFi analytics data including yield markets, PT/YT token data, and liquidity pool metrics

Retrieves the long/short ratio for crypto assets, indicating market sentiment and trader positioning on derivatives markets

Screens crypto tokens using DeFi analytics and on-chain intelligence to surface safety signals, market data, and risk assessments

Calculates period-over-period changes in token holder-address density (holders per million USD of market cap) for a supplied sequence of token observations.