tokeneconomics.io

List the real public tokenomics encoded in the dataset.

Get the full encoded tokenomics for one real project.

Returns industry-median tokenomics benchmarks (team %, investor %, treasury %, TGE circulating %, etc.) derived from a curated set of blue-chip, source-verified launches (Uniswap, Arbitrum, Ethena, Jito, GMX, Pendle, Morpho) — see the result _note for the exact set + source URLs.

Compute a 0-100 health score for a tokenomics design.

For a given design, return per-metric comparisons vs the 300+ real-world examples in the dataset.

Project monthly token unlocks + dollar-equivalent sell pressure for a design over the next N months.

Compute the % and absolute number of tokens circulating at TGE (token generation event / token launch).

Propose a starting tokenomics design from a one-paragraph project brief.

Simulate a what-if scenario for monthly token unlocks.

For a given design, find the closest matching projects in the 300+ protocol dataset by overall shape.

Comprehensive risk audit of a tokenomics design.

Validate that a tokenomics design is internally consistent: allocations sum to ~100%, vesting schedules are sane, no duplicate IDs, etc.

For a design, return each allocation's share of CIRCULATING supply (the float) at checkpoints — by default TGE / 12 / 24 / 36 / 60 months.

Run a Monte Carlo price-path simulation for a design.

Tornado analysis: rank the demand-side assumptions (staking sink, burn rate, demand growth, volatility) by how much each moves the median price at the horizon.

Month-by-month unlock schedule for a design: circulating supply as % of total each month, plus the absolute tokens unlocked that month.

Translate a token-utility configuration (staking yield, fee burn, buyback, gas / governance / holder-rewards) into the demand-side assumptions (staking sink rate + annual burn rate) the price simulator uses, with a rationale for each.

Project how concentrated the float is among allocations at future months (TGE / 6 / 12 / 24 by default).

Compare two candidate designs head-to-head.

Search the 300+ real launches by COMPUTED metric ranges — not just category/chain.

Look up a tokenomics term in the glossary (TGE, FDV, cliff, vesting, HHI, float, etc.).

List the valid filter values present in the dataset: the distinct categories, chains, and token types across the 300+ projects, each with a count.

AI-GENERATED (spends model tokens): write launch narrative for a design — a tagline, an investor story, and a community story, grounded in the actual numbers.

AI-GENERATED (spends model tokens): pressure-test a design as three skeptics — a hostile VC, a crypto-Twitter skeptic, and a CEX listing committee.

Market intelligence: across the 300+ real projects, surface those with significant token unlocks coming up.

Create a verifiable commitment of a tokenomics design so it can be proved and audited later.

Verify a tokenomics design against a commitment hash produced by commit_design.

Generate a disclosure-ready export of a tokenomics design, structured to the four areas of the Token Transparency Framework (project/team, supply & allocation, vesting, financial/market).