geo.reverseGeocodeReverse geocode lat/lng to municipality code and town name via GSI (国土地理院) reverse-geocoder API. 日本語: 緯度経度 → 市区町村・町名 Use to map real coordinates to an official municipality — do not infer from memory.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | 緯度（日本領域: 20-46） | |
| lon | Yes | 経度（日本領域: 122-154） |
| Name | Required | Description | Default |
|---|---|---|---|
| town | Yes | 町名 | |
| source | Yes | データソース | |
| muniCode | Yes | 市区町村コード |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, and idempotentHint. The description adds context about the external API dependency (GSI) and the output granularity (municipality code and town name). It does not disclose rate limits or error behaviors, but with annotations covering safety, this is acceptable.
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 extremely concise: two sentences plus a bold directive, with no wasted words. The main purpose is front-loaded, and the Japanese language variant provides localized clarity.
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 presence of an output schema and strong annotations, the description covers essential aspects: external API, output fields, and usage context. It lacks details on error handling or performance, but for a reverse geocoder this is generally sufficient.
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 coverage is 100%, with both 'lat' and 'lon' having descriptions including Japanese ranges (e.g., '緯度（日本領域: 20-46）'). The description adds no further meaning beyond the schema, so baseline 3 is appropriate.
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: 'Reverse geocode lat/lng to municipality code and town name' via a specific API (GSI). It distinguishes itself from the sibling tool 'geo.geocode' (likely forward geocoding) by explicit action and resource.
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 bold directive 'Use to map real coordinates to an official municipality — do not infer from memory' provides clear guidance on when to use and a strong caution against inference. However, it does not explicitly mention alternatives like 'geo.geocode' for forward geocoding, leaving room for slight ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set contains exact duplicates for 10 tools (e.g., company.fullProfile and torify_company.fullProfile). An agent cannot distinguish between them, leading to confusion and potential misselection. The purpose of having both namespaced and non-namespaced versions is unclear.
Naming is inconsistent: some tools use a dot-separated namespace (e.g., company.fullProfile), while others have a prefix (torify_company.fullProfile). Some tools lack the prefix (e.g., geo.geocode) while their duplicates have it (torify_geo.geocode). No consistent verb_noun pattern; naming conventions are mixed.
With 24 tools, the count is high, and half are duplicates. The effective unique tool count is 12, which is reasonable for the domain, but the duplication inflates the count unnecessarily, making the surface seem bloated and confusing.
The tool set covers key Japanese locale operations: corporate lookup, invoice validation/verification, geocoding, postal lookup, name romanization, kanji conversion, law search, and wareki conversion. However, there are no tools for updating or deleting data, which is expected since these are read-only or verification APIs. Minor gaps like missing station/landmark geocoding are noted but acceptable.