Japan Toilet & Accessibility

get_municipality_context

Read-onlyIdempotent

Official Japanese government data for any municipality, one call — housing vacancy (2003–2023), nearest-station ridership trend, hazard categories, land prices, livability counts. No scores, no judgment — official values only. Accepts a 5-digit municipality code (13104) or an exact name (Shinjuku-ku / 新宿区).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`fields`	No	Optional comma-separated subset: vacancy,ridership,population,hazard,land_price,livability.
`name_or_code`	Yes	5-digit 全国地方公共団体コード (e.g. 13104) or exact municipality name (Shinjuku-ku / 新宿区).

Output Schema

TableJSON Schema

Name	Required	Description
`hazard`	No	Hazard categories.
`vacancy`	No	Housing-vacancy counts (2003–2023).
`ridership`	No	Nearest-station ridership trend.
`land_price`	No	Published land prices near the centroid.
`livability`	No	Livability counts.
`population`	No	Population / future estimate.
`attribution`	No	Data source(s), license and provenance — an object, or an array of sources.
`municipality`	No	Resolved municipality + code.
`hazard_disclaimer`	No	Hazard usage disclaimer.

TDQS

A4.5/5.0

Behavior4/5

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

Annotations declare readOnly and idempotent. The description adds context: data is official, no scores/judgment, and 'one call' implies completeness. No contradictions, and additional behavioral context is provided.

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 two sentences. It front-loads the purpose and data types, then gives input details. No superfluous words – every sentence adds value.

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 output schema exists, the description covers the necessary scope: data categories, time range, input format. It is complete for the tool's 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?

Both parameters are fully documented in the schema. The description adds value with concrete examples (13104, Shinjuku-ku) and lists the field options, enhancing understanding beyond the schema.

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 Japanese government data for municipalities, listing specific data fields (housing vacancy, ridership, hazard, land prices, livability). It differentiates from sibling tools like get_station_context which focus on station-level data.

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 implies use when municipal-level official data is needed and provides input format guidance ('5-digit code or exact name'). However, it does not explicitly state when not to use it or compare to alternatives.

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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TDQS

A4.1/5.0

Disambiguation2/5

Several tools have overlapping purposes: get_active_alerts and get_station_alerts both return JMA flood/landslide alerts, differing only by input type. Similarly, get_municipality_context and get_station_context expose the same underlying data via different resolvers, and get_station_context already includes hazard categories that get_station_hazard duplicates in more detail. These overlaps make it difficult for an agent to know which tool to select for a given query.

Naming Consistency4/5

Most tools follow a clear 'get_' prefix (e.g., get_toilet_by_station, get_train_status), which is consistent. However, 'station_search' and 'ping' break the pattern, and the role-split text references a non-existent 'search_ramen' tool. The dominant convention is clear, but the exceptions introduce minor inconsistency.

Tool Count4/5

With 10 tools, the count is within a reasonable range for the server's broad scope (station info, toilet accessibility, hazards, alerts, train status). However, the redundancy between municipality_context/station_context and actives_alerts/station_alerts means not every tool earns a unique place, making the set slightly over-provisioned. Still, it is not excessively large.

Completeness3/5

The server covers the main info-retrieval needs for toilets, station hazards, alerts, and train status. However, station_search explicitly tells agents to 'search_ramen' for ramen details, yet that tool is not provided, creating a clear dead end. Also, the domain is broader than the server name suggests, and some peripheral data (e.g., detailed ramen profiles) is only partially surfaced.

Resources

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