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evidai

pay-per-call-mcp — USDC · x402 · AI Agent Billing | LemonCake

check_tax

Read-onlyIdempotent

Validate Japanese invoice registration numbers against NTA registry, determine source-withholding applicability, and compute withholding amount for AI/API payments. Ensures tax compliance in one call.

Instructions

Run a Japanese tax compliance check on a single transaction. No authentication required.

Performs three checks in one call:

  1. Validates the qualified-invoice registration number (T-number) against the NTA registry.

  2. Determines whether source-withholding (源泉徴収) applies based on the service description.

  3. If withholding applies, computes the withholding amount and net payable.

Intended for Japanese corporations that pay AI / API services and need to file withholding correctly under the qualified-invoice (インボイス制度) regime.

Returns: { invoice: { valid, name, ... }, withholding: { required, rate, amount, net } } Errors: invalid registrationNumber returns invoice.valid = false (not an exception).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
registrationNumberYesQualified-invoice registration number issued by the Japanese NTA (e.g. "T1234567890123").
serviceDescriptionYesPlain-text description of what was purchased. Used to classify whether source-withholding applies.
grossAmountJpyYesGross transaction amount in JPY, tax inclusive.
Behavior4/5

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

Discloses that errors return invoice.valid = false rather than exceptions, and explains three checks. Annotations already provide readOnly, idempotent hints; description adds useful context.

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?

Description is concise, uses bullet points for checks, and includes return format. Every sentence adds value without redundancy.

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?

Given 3 parameters, no output schema, and annotations, the description explains usage, behavior, and return structure comprehensively. No gaps.

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 coverage is 100% with descriptions for each parameter. Description adds minor context on how serviceDescription is used for withholding classification, but does not significantly enhance beyond schema.

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 clearly states it performs a Japanese tax compliance check on a single transaction, listing three specific checks. It is distinct from siblings like check_balance or list_services.

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?

Describes target users (Japanese corporations paying AI/API services) and states no authentication required. Does not explicitly mention alternatives, but siblings are unrelated.

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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