MLP Tax Computation Engine

mlp_info

Read-onlyIdempotent

Returns reference data for a supported MLP ticker — current cash distribution per unit, distribution growth CAGR, default return-of-capital percentage, distribution coverage ratio, K-1 entity count, operating-state count, and last-verified date.

Use when: User wants to look up baseline characteristics of an MLP before modeling — e.g., comparing distribution coverage across partnerships, checking how many K-1 entities a holding generates for tax-prep complexity, or seeing the operating-state count for state-tax filing-burden estimation.

Don't use for: Tax computation. Use mlp_projection (long-horizon modeling), mlp_estate_planning (estate analysis), mlp_sell_vs_hold (break-even sell price), or k1_basis_compute / k1_basis_multi_year (computing basis from actual K-1 data).

Note: This tool returns reference data only — no IRC citations apply, no methodology disclosure attached. For computation, use the modeling tools above.

Maintained by Lucas Andersen, MS Finance.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ticker`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds useful behavioral context: it returns reference data only, with 'no IRC citations' and 'no methodology disclosure attached'. It also notes the presence of a 'last-verified date' and maintenance owner, giving insight into data freshness and accountability.

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

Conciseness4/5

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

The description is well-structured with clear sections (what it returns, when to use, when not to use, note), but it is somewhat verbose and ends with a non-essential 'Maintained by' line. The content is valuable, but slight trimming would improve focus.

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?

The tool is simple (one parameter, no output schema), and the description fully compensates for the missing output schema by enumerating all return fields. It also provides comprehensive usage boundaries and alternative-tool references, making the description entirely self-contained for an agent.

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

Parameters4/5

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

Though schema description coverage is 0%, the sole parameter is 'ticker', which is inherently self-explanatory and further constrained by a detailed enum. The description reinforces that the ticker must be 'supported' and enumerates the returned data for that ticker, which indirectly clarifies what the parameter represents. No additional parameter-level details are needed.

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 description begins with a clear, specific action: 'Returns reference data for a supported MLP ticker' and enumerates the exact data fields (distribution, CAGR, return-of-capital, etc.), making the tool's purpose unmistakable. It also explicitly distinguishes itself from sibling computation tools by stating it provides 'reference data only'.

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 both explicit 'Use when' scenarios and a 'Don't use for' section with named alternative tools (mlp_projection, mlp_estate_planning, etc.), providing excellent guidance on when to choose this tool versus others.

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

All MLP Tax Computation Engine tools Inspect

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool targets a very specific and distinct use case: single-year basis, multi-year basis, long-term projection, estate planning, sell-vs-hold decision, and reference data lookup. The descriptions clearly state when to use each and (crucially) when not to, making selection unambiguous.

Naming Consistency4/5

Tool names mostly follow the pattern of domain prefix followed by the specific operation (e.g., k1_basis_compute, mlp_projection, mlp_sell_vs_hold). The naming is clear, though 'mlp_estate_planning' is a full phrase rather than verb_noun, which is a minor deviation from an otherwise strong pattern.

Tool Count5/5

With 6 tools covering basic reference, single-year basis, multi-year basis, projection, estate planning, and sell-vs-hold decision, the count is well-scoped for the domain. Each tool serves a clear purpose, and there is no bloat or missing critical functionality for the stated MLP tax and estate analysis use cases.

Completeness5/5

The tool set provides complete coverage of the MLP tax and estate planning lifecycle: retrieving reference data, computing single-year and multi-year basis, projecting future tax scenarios, comparing sell vs. hold, and analyzing estate step-up benefits. All major decision points and IRC sections are addressed, leaving no obvious gaps for a direct MLP holder.

Resources

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