Japan Toilet & Accessibility
Server Details
Structured data on wheelchair-accessible & public toilets across Japan for AI agents and travel/accessibility apps. 526 Tokyo stations (each mapped to its nearest station exit) + 612 municipalities. Station names accept Japanese or romaji. Free tier with API key.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 2 of 2 tools scored.
The two tools target completely different query types: one covers municipalities nationwide, the other covers Tokyo train stations. There is no overlap in purpose or input parameters, so an agent will never confuse them.
Both tools follow the 'get_<domain>_by_<criterion>' pattern with snake_case, making the naming predictable and easy to understand.
With only two tools, the server feels thin for a broad domain like Japan toilet accessibility. However, the tools are detailed and cover two key use cases (city and station), so the count is borderline acceptable.
The server lacks tools for other common queries (e.g., by prefecture, by amenity type, or near a point). The two tools cover major scenarios but leave notable gaps, such as searching outside Tokyo stations or non-station locations in cities.
Available Tools
2 toolsget_public_toilet_by_cityAInspect
List public toilets in a Japanese municipality, with wheelchair / baby-seat / ostomate flags, address and coordinates. Covers 612 municipalities nationwide (large cities capped at the top 50 results). Municipality names accept Japanese (e.g. 那覇市, 渋谷区); prefixing the prefecture improves accuracy.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | Municipality name in Japanese (e.g. 那覇市, 渋谷区, 上天草市). Prefix the prefecture for accuracy. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses coverage, result caps, and input format tips. However, it does not mention error handling, pagination, or any side effects, which is adequate for a read-only list tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no redundancy. The first sentence states the core purpose and output, and the second adds essential usage tips. It is front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no annotations, the description covers all essential aspects: purpose, output features, input format, coverage, and limitations. It is complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'city' is described in the schema, but the description adds extra context: 'accepts Japanese names' and 'prefixing the prefecture improves accuracy', providing value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and the resource 'public toilets in a Japanese municipality', including specific flags and coverage details. It distinguishes from the sibling tool 'get_toilet_by_station' by focusing on city-based search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides guidance on input format (Japanese names, prefixed prefecture), coverage (612 municipalities), and results cap (top 50 for large cities). It lacks explicit alternatives or exclusions but effectively guides correct use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_toilet_by_stationAInspect
Look up wheelchair-accessible / multipurpose toilets inside a Tokyo train station, including floor, gender, equipment (wheelchair, ostomate, diaper table) and the nearest exit. Covers 526 Tokyo stations. Accepts Japanese (新宿) or romaji (Shinjuku, Kita-Senju) for major stations.
| Name | Required | Description | Default |
|---|---|---|---|
| station | Yes | Station name in Japanese (新宿, 渋谷) or romaji for major stations (Shinjuku, Shibuya, Kita-Senju). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It explains the tool's scope (526 stations), input constraints (accepts Japanese/romaji for major stations), and returned info (floor, gender, equipment, nearest exit). This is adequate for a simple read-only lookup tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action and details. Every sentence adds value: first sentence explains function and outputs, second specifies scope and input formats. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no output schema), the description covers all essential aspects: what it does, inputs accepted, scope (526 stations), and what data it returns. It is complete for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and the description restates the same parameter info (station names in Japanese or romaji) without adding new semantics. Baseline is 3 because the schema already documents the parameter well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: looking up wheelchair-accessible/multipurpose toilets in Tokyo train stations. It specifies the resource (toilets), location (Tokyo stations), and details returned (floor, gender, equipment, nearest exit), distinguishing it from the sibling tool 'get_public_toilet_by_city' which likely covers city-level public toilets.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (for toilets inside Tokyo train stations) versus the sibling (city-level public toilets) and mentions input formats (Japanese or romaji). However, it does not explicitly exclude situations where the sibling tool would be more appropriate.
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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