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gf.sa_landed_cost

gf.sa_landed_cost

Saudi import landed-cost calculation. Price: 0.25 USDC via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
currencyNo
shippingNo
insuranceNo
referralCodeNo
vat_rate_pctNo
duty_rate_pctNo
product_valueYes
paymentSignatureNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.7/5.0
Behavior3/5

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

No annotations are present, so the description carries the burden of behavioral disclosure. It does disclose a critical behavioral fact: the tool costs 0.25 USDC via x402, a per-call charge. However, it does not mention side effects, what the action does with inputs, or what happens on payment failure, leaving significant behavioral ambiguity.

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 front-loaded with the purpose and is extremely efficient: two sentences, no filler. The second sentence about University price adds valuable factor, though the whole definition is too short to be a complete guide. But as a purely stylistic dimension, it is concise and non-repetitive.

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

Completeness1/5

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

Given an 8-parameter schema (one required), no output schema, and no annotations, this short description omits the returned value format, parameter meaning, calculation workflows, and any constraints. The agent is left to guess what luxury inputs like referralCode or paymentSignature are, and what a 'landed-cost calculation' actually produces. This is a major gap for a paid tool with that many inputs.

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

Parameters1/5

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

The description adds no meaning to any parameter. The schema has 0% parameter description coverage, and 8 properties (product_value, shipping, insurance, referralCode, vat_rate_pct, duty_rate_pct, currency, paymentSignature) go completely unexplained—an agent cannot infer how to fill them or compute the landed cost correctly.

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 states a specific resource and function: 'Saudi import landed-cost calculation.' The agent can infer the tool calculates landed costs for Saudi imports, which is a clear purpose. However, it does not differentiate this from sibling tools such as gf.gcc_merchant_pack or gf.marketplace_fee_quote, which may offer related costing or fee functions.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus any of the 20 sibling tools. It does not give scenarios, prerequisites, or exclusions such as 'use for Saudi imports only' or 'when payment is required.' The agent receives no hint about the expected context for invoking it.

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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