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koizumikento

JMA Data MCP

by koizumikento
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

get_weather_time_series

Get time-series weather observations for a JMA station to analyze trends over hours or days. Specify the station code, data interval, and duration to retrieve historical data points.

Instructions

Get time series weather data for a station.

Useful for analyzing weather trends over hours or days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNoNumber of hours to fetch (default: 24, max: 168 for ~1 week)
station_codeYesStation code (e.g., '44132' for Tokyo)
interval_minutesNoInterval between data points in minutes (10, 30, or 60)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / hours / description
      Added value: +"Number of hours to fetch (default: 24, max: 168 for ~1 week)"
    • addedInput schema / properties / interval_minutes / description
      Added value: +"Interval between data points in minutes (10, 30, or 60)"
    • addedInput schema / properties / station_code / description
      Added value: +"Station code (e.g., '44132' for Tokyo)"
  2. First observed

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention whether the operation is read-only, any authentication needs, or rate limits. It only restates information already in the schema.

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 concise, with two short sentences that are front-loaded and contain no unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description does not need to explain return values. However, it lacks usage guidance and behavioral transparency, making it minimally adequate.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides for the three parameters.

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 clearly states it retrieves time series weather data for a station, using a specific verb and resource. It is distinguishable from siblings like get_current_weather and get_forecast, though it does not explicitly differentiate.

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 says it is 'useful for analyzing weather trends over hours or days,' which implies when to use it, but it does not provide explicit when-not or alternative tool references.

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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