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Jma

Jma Forecast

jma_forecast
Read-onlyIdempotent

Weather forecast for a Japanese region from the Japan Meteorological Agency (JMA), covering a 3-day short-term outlook (weather, precipitation probability, temperature per sub-area) and a 7-day weekly outlook (weather, precipitation probability, reliability, min/max temperature with confidence range). Sourced from www.jma.go.jp/bosai/forecast/data/forecast/.json. Accepts a place name in English or Japanese ("Tokyo", "Sapporo", "大阪") or a JMA office code directly — resolved the same way as jma_resolve_area. Weather text is native Japanese; an English gloss is included for JMA's fixed weatherCode set (weatherEn), derived from a static translation table, not machine-translated on the fly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaYesPlace name or JMA area/office code, e.g. "Tokyo", "Fukuoka", "130000".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the readOnly/openWorld/idempotent annotations: output contents, source URL, Japanese-native weather text, and the static weatherCode translation table. It also clarifies that the English gloss is not machine-translated, which is valuable for setting expectations.

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 front-loaded with the core purpose, then efficiently expands into coverage details, input forms, source, and localization behavior. Every sentence contributes useful information without redundancy.

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?

Despite having no output schema, the description adequately describes what callers should expect: weather, precipitation probability, temperature, reliability, and confidence ranges. The single-parameter tool is fully specified for correct invocation, including examples and accepted input formats.

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% and the schema already explains the 'area' parameter, but the description adds extra semantic value by stating that English, Japanese, or JMA office codes are accepted and that resolution follows the same rules as jma_resolve_area. This helps an agent form correct invocations.

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 identifies the tool as a JMA weather forecast for Japanese regions, explicitly covering both 3-day and 7-day outlooks. It distinguishes itself from sibling tools like jma_warnings and jma_earthquakes by naming the exact resource and scope.

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

Usage Guidelines4/5

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

The description makes clear when this tool is relevant: when a JMA weather forecast is needed. It also references jma_resolve_area for resolution behavior, giving the agent useful routing context. However, it does not explicitly state conditions for when NOT to use it or name alternatives for warnings/earthquakes.

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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