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by io.github.Invaro
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is_tipped_occupation

Determine whether a job is on the Treasury Tipped Occupation list (Treas. Reg. § 1.224-1, final Apr 2026) for the § 224 'no tax on tips' deduction. Fuzzy-matches the job name; returns the official listing (name, TTC code, category) or a definitive 'not listed'.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "job": "<job>"
    },
    "name": "is_tipped_occupation"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobYese.g. "bartender", "software engineer", "DJ"

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / job / maxLength
      Added value: +500
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose the key non-obvious behavior: matching is fuzzy, not exact, and the tool returns either an official listing record or an explicit negative. It omits match-threshold/ambiguity behavior and any rate or permission notes, which is a minor gap for a read-only reference lookup.

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?

Two sentences, zero filler, and the core question is stated first with the citation and return-shape following. Every clause earns its place by adding either authority, behavior, or output expectation.

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

Completeness4/5

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

For a single-parameter read lookup with no output schema, the description is nearly sufficient: it explains what a positive and negative result look like (name, TTC code, category vs. 'not listed'). Only the handling of near-miss or multiple matches is left unaddressed.

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?

Schema coverage is 100% and the example values ('bartender', 'software engineer', 'DJ') already frame the input, so the schema does heavy lifting. The description still adds real semantics by saying the job name is fuzzy-matched, which tells the agent it need not supply an exact official title.

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 names a specific verb and resource ('Determine whether a job is on the Treasury Tipped Occupation list'), pins it to the exact legal authority (Treas. Reg. § 1.224-1) and statutory purpose (§ 224 'no tax on tips' deduction). No sibling tool overlaps with this lookup, and an agent can tell exactly what question this answers.

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

Usage Guidelines4/5

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

The § 224 deduction framing gives clear context for when to reach for it, and the 'returns ... or a definitive not listed' clause implies it can be used for negative checks. It does not state exclusions or point at a related tool (e.g. explain_rule for the underlying regulation), but with no comparable sibling the routing is unambiguous.

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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