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mouseart2025

china-context-mcp

by mouseart2025

Holiday Summary

holiday_summary

Get China's annual holiday summary for a year: continuous holiday periods, statutory 3x pay days, make-up workdays, and total rest days for leave planning and payroll.

Instructions

返回中国某年的节假日与调休摘要——聚合推导,不是单日查询。

与 holiday_info 的区别:本工具对 timor 全年扁平数据做聚合,给出 holiday_info 拿不到、而 AI 自身容易算错的结论——连续假期区间与天数、 法定 3 倍工资天数、全部调休补班日、全年总休假天数。

参数 year:4 位年份,省略则用当前年。 适用:年度休假规划、考勤与排班、HR 与薪酬核算。 数据来自 timor.tech(公开、零凭证)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
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 the data source (timor.tech, public, zero credentials), the fact that it is a read-only aggregation (no mutation implied), and the specific derived outputs (continuous holiday intervals, statutory 3x pay days, adjusted workdays, total annual leave days). It doesn't state response format details, but an output schema exists to cover that.

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 tightly written with zero filler. The core purpose is front-loaded in the first line, followed by sibling differentiation, then the derived outputs, then parameter and applicability notes. Every sentence earns its place.

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?

The tool is simple (one optional parameter), an output schema exists to explain return values, and the description covers purpose, differentiation, aggregation results, parameter semantics, and data source. Nothing an agent needs to call it correctly is missing.

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 0% and the only parameter (year) has no description in the schema, but the description compensates: it states the parameter is a 4-digit year and that omitting it defaults to the current year. This adds real meaning beyond the bare integer type.

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?

Description states a specific verb+resource+scope: aggregates Chinese holidays and adjusted workdays for a year. The first line explicitly contrasts with single-day queries and names the sibling (holiday_info) it complements, so an agent can distinguish them immediately without inspecting either schema.

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

Usage Guidelines5/5

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

Explicitly names the sibling tool holiday_info as the alternative and states the key difference (aggregation of flat yearly data vs single-day lookup). Also enumerates concrete use cases (annual leave planning, attendance/scheduling, HR and payroll), giving clear when-to-use guidance.

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