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Priceminder MCP Server

Priceminder MCP Server

by haidrau

create_decision

Record a competitor pricing decision with action, target price, and reason, then set a 14-day review to track outcomes and support adoption or rejection.

Instructions

记录一条跟卖决策建议(状态为 suggested,14 天后到期复盘)。决策会进入统计与复盘闭环。

用户可能的问法:

  • "记一下我的决定:跟着降到 199"

  • "这个商品我打算维持价格,记一条"

  • "把系统的建议存成一条决策"

参数:

  • monitor_id: 监控项 ID

  • action: 必须是 follow_price / hold_price / observe / raise_price / improve_rating

  • target_price: 目标价(follow_price / raise_price 时建议填写)

  • reason: 留档理由(自由文本)

  • context_json: 决策时上下文(JSON 字符串;传对象会自动序列化)

返回 decision_id 与 review_at;随后可用 adopt_decision 采纳或 reject_decision 拒绝。

Input Schema

NameRequiredDescriptionDefault
actionYes
reasonNo
monitor_idYes
context_jsonNo
target_priceNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose real behavior: the created record defaults to status 'suggested', expires for review after 14 days, and feeds statistics/review loops. It also documents the return payload (decision_id, review_at). It does not mention permissions, whether duplicate decisions are allowed, or failure modes.

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?

Front-loaded with the core behavior, then parameter semantics, then return values — a logical reading order. The user-phrasing bullet block is mildly redundant but does add intent-matching value; the rest is tight.

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?

For a 5-parameter mutation tool with no annotations, it covers purpose, all parameters, side effects on status/expiry, and return fields (an output schema exists so return details need not be exhaustive). Missing only edge-case guidance such as valid monitor_id preconditions or duplicate handling.

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

Parameters5/5

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

Schema coverage is 0% and no enum is declared, so the description does essential work: it enumerates the five valid action values, states target_price is expected for follow_price/raise_price, and explains that context_json accepts an object and will be serialized. That compensates fully for the empty schema descriptions.

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 states a specific verb and resource (record a follow-sell decision, status=suggested, 14-day review), plus the downstream lifecycle. It also names adopt_decision and reject_decision as the follow-up actions, so an agent can separate creation from adoption/rejection without opening other schemas.

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 example user utterances ("记一下我的决定:跟着降到 199") map natural-language intents to this tool, which is unusually actionable guidance. It also describes the workflow continuation via adopt_decision/reject_decision, but never states when NOT to use it (e.g., versus review_decision or when a decision already exists).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.