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luckin-mcp-proxy

luckin-mcp-proxy

by wyhAcc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

quickOrder

Turn a natural language drink description into an order preview with draftId for confirmation.

Instructions

一句话点瑞幸：自然语言描述饮品（如「大杯冰美式不加糖」），自动用常用门店和口味偏好，返回订单预览和 draftId。不会直接下单，需用户确认后调 confirmOrder。

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": {
      "query": "<query>"
    },
    "name": "quickOrder"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes饮品描述，可含杯型/温度/糖度
shopNameNo门店名，缺省用常用门店
amountNo杯数，默认 1

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool does not directly place orders, returns a preview and draftId, and uses default store and preferences. This is sufficient disclosure for a preview tool.

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 a single concise Chinese sentence that front-loads the main purpose and key behaviors. Every word adds value with zero waste.

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?

Given no output schema, the description adequately explains the return: order preview and draftId, and the overall flow. It is complete for a draft-creating tool, though it does not cover error handling for invalid queries.

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 description coverage is 100%, so baseline is 3. The description adds context about the default behavior for shopName and amount, which is not in the schema. It also clarifies the query parameter's role, providing marginal but useful value.

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 the tool's purpose: it takes a natural language description of a drink, auto-fills store and taste preferences, and returns an order preview with draftId. It also clarifies that it does not place the order directly, distinguishing it from sibling tools like confirmOrder.

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 explains when to use the tool (quick order draft via natural language) and implies that for final placement, confirmOrder should be used. It mentions default store and preferences, providing context, but does not explicitly list alternative tools or when not to use 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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