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JMA Data MCP

JMA Data MCP

by koizumikento
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Get Forecast

get_forecast
Read-onlyIdempotent

Retrieve weather forecast data for a specified Japanese prefecture from JMA to check expected local conditions for planning or travel.

Instructions

Get weather forecast for a prefecture.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prefecture": "<prefecture>"
    },
    "name": "get_forecast"
  }
}

Input Schema

NameRequiredDescriptionDefault
prefectureYesPrefecture name in English (e.g., 'tokyo', 'osaka', 'hokkaido_sapporo')

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.2.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / prefecture / description
      Added value: +"Prefecture name in English (e.g., 'tokyo', 'osaka', 'hokkaido_sapporo')"
  2. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds no behavioral detail beyond that, such as forecast horizon, freshness, or whether results are location- or time-based, but it does not contradict the annotations.

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?

The description is a single front-loaded sentence with no wasted text. It is appropriately concise, though its brevity leaves important usage distinctions unaddressed.

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?

An output schema exists and the single parameter is fully documented, so return value and input details need not be repeated. However, for a forecast tool in a crowded weather-tool family, the description is not complete enough to distinguish forecast scope or select this tool confidently.

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 prefecture parameter's English-name format and examples are fully documented in the schema. The description refers to the prefecture concept but adds no syntax, constraints, or meaning beyond what the schema already provides.

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 clear verb and resource: get a weather forecast for a prefecture. It does not differentiate the tool from close siblings such as get_current_weather, get_weather_time_series, or get_historical_weather, so an agent cannot tell from the description alone which weather query is intended.

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?

The description provides no guidance on when to use this tool versus alternatives. With siblings covering current weather, location-based weather, time series, and historical weather, the absence of any when-to-use or when-not-to-use instruction is a meaningful gap.

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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