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clmmaxt

Retrieve daily maximum temperature records for a specified station and year, with optional month filtering. Data available from 1884 to present.

Instructions

Daily Maximum Temperature (CLMMAXT) API Request

Parameters:

  • station: Station code (refer to documentation for full list)

  • year: Year (1884 - current year, station-specific range)

  • month: Month (1-12, optional but requires year)

  • rformat: 'json' or 'csv' (Response format, default: csv)

Request Example: https://data.weather.gov.hk/weatherAPI/opendata/opendata.php?dataType=CLMMAXT&station=HKO&year=2025&rformat=json

Response Keys (JSON format):

  • type: Array with data type ["Max Temperature"]

  • fields: Array with field names ["Year", "Month", "Day", "Temperature(C)"]

  • data: Array of arrays with temperature data

  • legend: Array with station information

Response Keys (CSV format):

  • Type: Data type line

  • Header row: Year,Month,Day,Temperature(C)

  • Data rows: Actual temperature data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stationYesStation code (e.g., HKO for Hong Kong Observatory)
yearYesYear between 1884 and current year
monthNoOptional month (1-12) when specific month data is needed
rformatNoOutput format: 'json' or 'csv' (default: 'json')json
Behavior4/5

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

No annotations are provided, so the description carries full burden. It explains the output format for both JSON and CSV, including the structure of keys and data arrays, which is transparent for a read operation.

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 well-structured with sections for parameters, example, and response keys. Every part serves a purpose without redundancy, and it is appropriately concise for the complexity.

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 the lack of output schema, the description provides comprehensive response format details. It could mention error handling or rate limits, but for a simple data retrieval tool, it is largely complete.

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%, but the description adds value with an example URL, explanation that month is optional, and a reference to a station code list, going beyond the schema.

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 it is the 'Daily Maximum Temperature (CLMMAXT) API Request' and includes an example URL and response keys, making the tool's purpose unmistakable.

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

Usage Guidelines3/5

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

The description explains parameters and provides an example, but does not explicitly guide when to use this tool versus sibling tools like clmtemp or other weather APIs.

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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