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votsie
by votsie

Статус заказа Steam

wata_dg_steam_order_get

Check a Steam top-up order's status by providing the order ID and payment method (acquiring or deposit) to retrieve the current state.

Instructions

Проверяет статус заказа на пополнение Steam. Укажите тот же payment, что и при создании: у депозитных заказов статус лежит по общему адресу, а не по адресу направления.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesИдентификатор заказа
paymentYesacquiring — платит покупатель; deposit — списывается с депозита мерчанта

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description must carry the behavioral disclosure burden. It reveals that deposit orders have a distinct status location ('at the common address, not at the direction address'), which is a useful behavioral trait. However, it does not explicitly state that the operation is read-only, nor does it describe the return format or error conditions. It adds some behavioral context but is not comprehensive.

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 two sentences with no filler. It front-loads the purpose and then adds the critical usage nuance. Every sentence earns its place; it is efficient and well-structured.

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?

For a simple 2-parameter status check, the description covers the core purpose, parameter consistency, and a subtle behavioral difference. However, it omits any information about the response format or possible error states, which an agent might need to parse the result. Given the low complexity and no output schema, this is a minor gap but still leaves the agent without a full picture of what to expect.

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% with descriptions for both parameters, so baseline is 3. The description adds value by instructing that 'payment' must match the one used at creation and explains the deposit-specific behavior. This goes beyond the schema's enum definitions and provides operational context that helps the agent use the parameter 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 clearly states the tool checks the status of a Steam top-up order, which is a specific verb-resource pairing. It distinguishes from related tools like wata_dg_steam_order_create and wata_dg_stars_order_get by focusing on Steam top-up orders, though it does not explicitly name siblings. The purpose is unambiguous.

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 description gives explicit guidance on the 'payment' parameter: 'Specify the same payment as when creating' and explains the deposit nuance about status location. This tells the agent how to use the tool correctly, though it does not discuss when to prefer this tool over other get-order tools. The parameter-specific guidance is valuable and contextually relevant.

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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