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Gachi Data API — Japan Station & Accessibility Data

by com.gachi-tokusuru

get_active_alerts

Read-onlyIdempotent

Live river flood forecasts and landslide alerts for Japan (JMA official). NOT general weather warnings (storm/heavy rain/snow) and NOT earthquakes. Covers JMA 指定河川洪水予報 (river flood forecast, levels 2–5) and 土砂災害警戒情報 (landslide warning), each with level, affected area, official summary and issue time. Optional area filters by 2-digit prefecture code (e.g. 13 = Tokyo) or a JMA forecast-area code. Relay of official facts — not a warning issued by this service, not a life-safety system.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaNoOptional prefecture code (01–47, e.g. 13 = Tokyo) or JMA forecast-area code.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of active alerts.
staleNoTrue if the snapshot is stale.
alertsNoActive JMA river-flood / landslide alerts with level, area, summary, issue time.
sourceNoSource label.
coverageNoWhat this feed covers — string or array of categories.
disclaimerNoRelay disclaimer (not a warning issued by this service).
fetched_atNoWhen the snapshot was fetched.
attributionNoData source(s), license and provenance — an object, or an array of sources.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already convey readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context about the data source (JMA official), the specific alert types covered, the fields returned (level, affected area, summary, issue time), and a disclaimer that it is not a life-safety system, going beyond 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 concise and front-loaded with the core purpose. It uses four sentences, each adding value: purpose/exclusions, coverage details, parameter explanation, and a disclaimer. It is dense but not overly lengthy, with no redundant fluff.

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?

Given the tool has one optional parameter, strong annotations, and an output schema, the description sufficiently covers the data source, alert types, returned fields, filtering option, and important caveats. This is complete for an AI agent to select and invoke the tool correctly.

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% for the single `area` parameter, so the baseline is 3. The tool description repeats the filter functionality and examples (13 = Tokyo) but adds little beyond the schema's own description, which already explains the parameter is optional and examples are given.

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 'Live river flood forecasts and landslide alerts for Japan (JMA official)' with explicit exclusions ('NOT general weather warnings... and NOT earthquakes'), distinguishing it from sibling tools like get_station_alerts. The verb 'get' plus specific resource and scope makes purpose unambiguous.

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?

Provides clear context for when to use: it covers JMA river flood and landslide alerts, and explicitly excludes other warning types and earthquakes. It also describes the optional area filter and the service's role as a relay, not a life-safety system. However, it does not name alternative tools or state 'use this when...' beyond exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Gachi Data API — Japan Station & Accessibility Data toolsInspect
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TDQS

A4.3/5.0
Disambiguation3/5

Several tools overlap in function: get_active_alerts and get_station_alerts both return the same JMA alerts but with different input types, and get_municipality_context and get_station_context provide identical municipality data. However, the descriptions clarify the input differences, and other tools like get_toilet_by_city vs get_toilet_by_station are distinct enough.

Naming Consistency4/5

Most tools follow a consistent 'get_<noun>' pattern (get_active_alerts, get_municipality_context), but 'ping' and 'station_search' deviate from this convention. Still, the naming is largely predictable and readable.

Tool Count5/5

With 10 tools, the server is well-scoped for its purpose of station and accessibility data. Each tool serves a distinct function within the domain, and the count is within the ideal 3-15 range.

Completeness4/5

The tool surface covers the core workflows: discovery (station_search), station details (context, hazard, toilet), live alerts, and train status. Minor gaps exist such as the lack of a direct station info tool or the mention of 'search_ramen' which is not actually provided, but these are not critical.

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