mlp-tax

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

Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds meaningful context beyond them: 'reference data only', 'no IRC citations apply, no methodology disclosure attached', and the presence of a 'last-verified date' which signals data freshness. This substantially clarifies what the tool does and does not provide, with no contradiction.

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-organized and front-loaded: core output list first, then usage guidance, then exclusions and caveats. It is slightly verbose with minor redundancy ('use the modeling tools above' repeats the don't-use list) and the 'Maintained by' line adds little to tool selection or invocation.

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?

Even without an output schema, the description enumerates all return fields, provides realistic use cases, and explicitly sets behavioral boundaries (reference data only, no IRC/methodology). For a simple one-parameter lookup with strong annotations, this is sufficient for an agent to invoke it 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 only parameter, ticker, is fully constrained by the schema enum (8 supported tickers), and the description calls it a 'supported MLP ticker'. However, the prose adds little semantic detail beyond the schema's enumeration, and with 0% schema description coverage, the description only minimally compensates.

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 opens with 'Returns reference data for a supported MLP ticker' and enumerates the exact fields returned (cash distribution, CAGR, return-of-capital percentage, coverage ratio, K-1 entity count, operating-state count, last-verified date). This specific verb+resource+scope clearly distinguishes it from sibling modeling/computation tools.

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 provides explicit 'Use when' scenarios (baseline characteristic lookup, distribution coverage comparison, K-1 complexity assessment) and a 'Don't use for' section that names alternatives: mlp_projection, mlp_estate_planning, mlp_sell_vs_hold, k1_basis_compute, k1_basis_multi_year. This gives an agent clear decision rules for tool selection.

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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