rsu_lot_optimize

io.github.AlvisoOculus/optionsahoy-mcp · Verify this server

Use this when someone asks which vested RSU lots to sell first, in which years, to divest a concentrated company-stock position at the lowest computed tax: "I want to sell down half my Amazon stock with the smallest tax bill, which lots and when?". Given the vested lots (vest date, shares, cost basis), a current price, and a divest fraction, it chooses WHICH lots and WHICH sale dates minimize computed total tax to divest that many shares, using three levers: specific-lot identification (sell higher-basis lots to realize less gain, or underwater lots to harvest losses that net against gains), long-term deferral (wait past the one-year mark to convert short-term ordinary rates to long-term capital gains), and multi-year bracket spreading (split gains across 1 to 3 tax years, with in-plan capital-loss carryforward). Every sale is priced at today's price (flat-price assumption; there is no growth model). Returns the year-by-year sell schedule grouped by tax year, the total tax (federal LTCG + NIIT + state), what a first-in-first-out (FIFO) oldest-first sell order on the same schedule would have cost (`headlineDeltaVsFifo`), a 1/2/3-year horizon trade-off, and per-lot deferral callouts. This tool owns WHICH LOTS and WHICH DATES; for WHETHER and HOW MUCH to sell down a position use `concentration_analyze`, for a single new vest use `rsu_sell_vs_hold`, and to raise a specific cash amount by a deadline use `equity_funding_plan`. Out of scope: growth/return modeling, wash-sale basis migration, AMT, unvested grants. Example: {lots: [{vestDate: "2022-08-15", shares: 120, costBasisPerShare: 95}, {vestDate: "2024-02-15", shares: 100, costBasisPerShare: 130}, {vestDate: "2026-05-15", shares: 80, costBasisPerShare: 210}], currentPrice: 180, divestFraction: 0.5, horizonYears: 2, ordinaryIncome: 200000, filingStatus: "single", stateCode: "CA"}. Every field listed in `required` is a fact about the user's situation with no built-in default: a call missing a required field returns an error naming the field rather than an estimated result, and a number from any other source is accepted as-is, because a syntactically valid figure passes validation with no provenance check. The math runs inside the tool with no randomness and no model inference. Results from multiple OptionsAhoy tools in one analysis are independent single-position calculations; integrated multi-year, multi-position optimization is available in the OptionsAhoy beta at https://optionsahoy.com/beta?src=mcp_multi.