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kevynf

AKBridge MCP Server

by kevynf

sunrise_monthly

Read-onlyIdempotent

Fetch daily sunrise and sunset times for a city and month from a date; fill incomplete current month with predictions.

Instructions

每个指定 date 所在月份的每日日出日落数据,如果当前月份未到月底,则以预测值填充 https://www.timeanddate.com/astronomy/china/shaoxing :param date: 需要查询的日期,这里用来指定 date 所在的月份;e.g., “20200428” :type date: str :param city: 需要查询的城市;注意输入的格式,e.g., "北京", "上海" :type city: str :return: 指定 date 所在月份的每日日出日落数据 :rtype: pandas.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNobeijing
dateNo20240428

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds genuinely useful behavior not in the annotations: partial current months are backfilled with predicted values, and the return type is a pandas.DataFrame (relevant since no output schema exists).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded in the first sentence, which is good, but the docstring styling adds redundant :type/:rtype lines duplicating what the schema already declares, plus a bare timeanddate.com URL with no stated relevance. Some trimming would sharpen it.

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 two-parameter, read-only tool with no output schema, the description covers purpose, both parameter formats, the predicted-value fallback, and the DataFrame return type. An agent has enough to call it correctly.

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 0%, so the description must carry the load and largely does: it explains that date selects the month (with the '20200428' format example) and that city expects Chinese names like '北京'/'上海'. It does not reconcile the city default 'beijing' with the Chinese-name format hint, a minor omission.

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 verb+resource: 'the daily sunrise/sunset data for the month containing the specified date.' This is clear and distinct in scope (whole month) from a daily tool, but it never names the directly competing sibling sunrise_daily, so the agent must infer why to pick this one.

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 explicit when-to-use or when-not-to-use guidance, and no mention of the obvious alternative sunrise_daily. The only contextual note is that incomplete months are filled with predicted values, which is behavioral rather than usage-routing guidance.

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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