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Jp Dependent Threshold

jp_dependent_threshold
Idempotent

Income-wall analysis: tax, social insurance (1.06M/1.30M), spousal and student-dependent walls. $0.02/call. Deterministic, cites primary sources; errors and unverified rules are never billed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoCalendar year
as_ofNoCalculation reference date YYYY-MM-DD (default today); selects the rules version
x_paymentNoOptional: base64 X-PAYMENT header value (x402 exact scheme, USDC on Base mainnet). Omit it to receive the payment requirements (accepts[] + docs) for this call without being charged.
annual_income_yenYesExpected annual income in yen

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / annual_income_yen / description
      Previous value: -"See the product's input spec in /catalog"New value: +"Expected annual income in yen"
    • addedInput schema / properties / as_of / format
      Added value: +"date"
    • addedInput schema / properties / year / default
      Added value: +2026
  2. Changed3 schema fields changed
    • addedInput schema / properties / annual_income_yen / description
      Added value: +"See the product's input spec in /catalog"
    • addedInput schema / properties / as_of / description
      Added value: +"Calculation reference date YYYY-MM-DD (default today); selects the rules version"
    • addedInput schema / properties / year / description
      Added value: +"Calendar year"
  3. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare non-read-only, open-world, idempotent, and non-destructive hints. The description adds valuable behavioral context: 'deterministic, cites primary sources' and the billing policy of never charging for errors. This goes beyond the annotations and helps the agent trust the output quality.

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 extremely concise and front-loaded with the core purpose ('Income-wall analysis'), followed by cost and behavioral notes. Every clause adds value with zero filler. Ideal structure for an AI agent.

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

Completeness3/5

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

The tool has no output schema, so the description should at least hint at what the agent can expect in return. It does not mention result format, error behavior, or any limitations beyond billing. While the purpose is clear, the lack of return-value context leaves a gap for a computation tool with such niche functionality.

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?

Schema description coverage is 100%, so all four parameters are already well-documented. The description mentions the types of income walls, which indirectly relates to annual_income_yen, but it does not add syntax or format details beyond the schema. Baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool performs 'income-wall analysis' covering tax, social insurance (1.06M/1.30M), spousal and student-dependent walls. This is specific and distinguishes its scope from generic tax tools, though it does not explicitly name sibling alternatives.

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

Usage Guidelines3/5

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

The context 'income-wall analysis' implies when to use it, but there are no explicit exclusions, alternatives, or conditions for selecting this tool over siblings like jp_social_insurance or jp_payroll_net. The description gives no direct comparison.

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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