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mlp-tax

by ai.lucasandersen.api

mlp_projection

Read-onlyIdempotent

Computes a multi-year tax projection for a publicly traded MLP position, applying the IRS Partner's Basis Worksheet methodology (Lines 1-14) per IRC §705 (basis computation), §731(a) (distributions exceeding basis), §733 (basis reduction), §751 (hot asset recapture), §752 (liability allocation), §1014 (stepped-up basis at death), and §199A (QBI deduction). Returns year-by-year basis erosion, §751 accumulation, annual federal tax, terminal FMV, §1014 step-up value at death, and the break-even sell price.

Use when: User holds direct units of a midstream MLP (EPD, ET, MPLX, WES, PAA, NRP, USAC, SUN) and wants to model long-term tax outcomes — when basis reaches zero, total tax paid over the hold horizon, deferred tax eliminated by §1014 step-up at death, or the unit price at which selling matches holding through inheritance. Single position, single lot.

Don't use for: 1099-DIV ETFs (AMLP, MLPX, AMZA — these use RIC structure, pay corporate-level tax, and issue 1099-DIV instead of K-1; use a standard cost-basis calculator instead). Multi-position estate analysis — use mlp_estate_planning. Computing basis from actual K-1 data the user has in hand — use k1_basis_compute (single year) or k1_basis_multi_year.

Limitations: Single position, single lot — for multi-position portfolios and per-lot optimal sell ordering, see lucasandersen.ai. Federal-level only — does not include state-level basis adjustments or state estate tax. §751 recapture is estimated from default ROC assumptions; actual recapture depends on the partnership's hot-asset disposition schedule.

Maintained by Lucas Andersen, MS Finance, with direct positions in major midstream MLPs. Methodology auditable at lucasandersen.ai/methodology.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
unitsYesNumber of MLP units held
yearsNoProjection horizon in years (1-50, default 20)
tickerYesMLP ticker symbol
tax_bracketNoFederal marginal rate as decimal, e.g. 0.32 (default 0.32)
purchase_priceNoPurchase price per unit in USD (optional — defaults to reasonable estimate)

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Even though annotations already declare readOnlyHint=true and idempotentHint=true, the description adds extensive behavioral context: limitations (single position, single lot, federal-only), estimation details (§751 recapture from default ROC assumptions), and what the return values are. This goes far beyond the annotation hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every section earns its place: purpose, return values, use cases, exclusions, limitations, and maintenance context. It is structured with clear paragraph breaks and front-loaded with the core computation, making it efficient despite the length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with no output schema, the description covers all necessary context: what it returns, when to use, when not to use, limitations, and methodology references. It even states the expected inputs (MLP tickers, single lot) and the absence of state-level calculations. This is comprehensive for an AI agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% coverage with clear descriptions for all 5 parameters. The tool description itself does not add additional parameter-level meaning beyond the schema, though it does reinforce constraints like 'single position, single lot' that relate to the tool's scope rather than individual parameters. Baseline 3 is appropriate given high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states a specific verb ('Computes') and resource ('multi-year tax projection for a publicly traded MLP position'), with detailed methodology. It also distinguishes from siblings by explicitly naming alternatives like mlp_estate_planning and k1_basis_compute, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes explicit 'Use when' and 'Don't use for' sections, providing clear context for when to choose this tool and when to select a sibling tool. It names exact alternatives (e.g., 'use mlp_estate_planning', 'use k1_basis_compute') and describes non-qualifying scenarios like 1099-DIV ETFs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: single-year basis, multi-year basis, estate planning, info lookup, long-term projection, and sell-vs-hold analysis. The 'Don't use for' sections further eliminate ambiguity, making it easy for an agent to select the correct tool.

Naming Consistency4/5

Tool names use a consistent snake_case convention with domain prefixes ('k1_' for basis tools, 'mlp_' for others). While the pattern is not strictly verb_noun (e.g., 'k1_basis_compute' has verb after noun, and 'mlp_estate_planning' is noun_gerund), the naming is clear and predictable, with only minor deviations from a uniform pattern.

Tool Count5/5

With 6 tools, the server is well-scoped for its domain of MLP tax calculations. Each tool addresses a specific, essential use case without redundancy, and the count is neither too sparse nor too heavy for a specialized tax calculator.

Completeness4/5

The tool set covers the core MLP tax scenarios: single-year and multi-year basis, long-term projection, estate planning, sell-vs-hold comparison, and reference data. A minor gap is the lack of multi-position or multi-lot optimization, but that is explicitly noted as beyond scope, and the server feels complete for its intended purpose.

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