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Gachi Data API — Japan Station & Accessibility Data

by com.gachi-tokusuru

get_station_context

Read-onlyIdempotent

Same official municipality data as get_municipality_context, resolved from a station: pass a station name (Shinjuku / 新宿 / Musashi-Kosugi) or a Japan Station Master station_id (e.g. st_00001), and it returns the context for that station's municipality. Official values only — no scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoOptional comma-separated subset: vacancy,ridership,population,hazard,land_price,livability.
station_idNoJapan Station Master station_id (e.g. st_00001). Alternative to station_name.
station_nameNoStation name in English/romaji (Shinjuku) or Japanese (新宿). Provide this or station_id.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hazardNoHazard categories.
stationNoResolved station.
vacancyNoHousing-vacancy counts.
ridershipNoRidership trend.
land_priceNoLand prices near the centroid.
livabilityNoLivability counts.
populationNoPopulation / future estimate.
attributionNoData source(s), license and provenance — an object, or an array of sources.
municipalityNoMunicipality + code.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context by specifying the data source (same official municipality data) and what is excluded ('no scores'), which goes beyond the structured annotations. It does not mention rate limits or error handling, but for a read-only lookup this is sufficient.

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?

Two sentences, front-loaded with the key reference to get_municipality_context. Every sentence adds meaningful information (input options, output type, data policy) without redundancy.

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 an output schema exists and annotations cover safety, the description explains the core use case well. It clearly indicates how to specify input (station name or ID) and what the output represents. It omits hypothetical edge cases, but overall it is complete for a tool of this complexity.

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 covers 100% of parameters with descriptions, providing a baseline of 3. The tool description adds valuable examples (Shinjuku / 新宿 / Musashi-Kosugi and st_00001) and clarifies the mutual exclusivity implicit between station_name and station_id, enriching the schema's bare parameter descriptions.

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 the tool returns official municipality context resolved from a station, with specific input examples (station name or station_id). It also distinguishes from get_municipality_context and clarifies 'no scores', making its 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?

It explicitly references get_municipality_context and explains the key difference—resolved from a station—which tells when to use this tool. It gives clear input instructions, though it does not explicitly state when not to use it or name alternative tools beyond the municipality counterpart.

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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