kuongeo

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

With no annotations, the description carries the behavioral burden. It adds useful facts: no API key is needed and results arrive in one call. However, it does not explicitly state that the operation is read-only or what happens when the place is not found, so some behavioral risk remains unaddressed.

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?

Four short sentences each add distinct information: input form, return payload, auth requirement, and routing to siblings. The examples are packed into the first sentence without bloat, and there is no repetition of schema details.

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?

Even though there is no output schema, the description lists the full return payload with units (km), mountpoint, host, and the 5-digit municipality code. It lacks only minor details like ambiguous-name behavior and lang output effect, which keeps it slightly below complete.

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 50%; q is documented in the schema, and the description adds concrete examples (帯広, Obihiro, 京都市の基準局) and signals that extra words like 基準局/警報/near are accepted. But lang is mentioned only as an enum in the schema; the description never explains how the response language differs, leaving that parameter under-specified.

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?

Description states a concrete resource (a Japanese place) and exactly what the one-shot call returns: municipality code, JMA warnings, nearest RTK base station, and page URLs. It also distinguishes itself from the sibling detail tools by saying 'Use this first' and pointing to here/rtk_bases_near.

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?

Explicitly says 'Use this first when a user asks about a Japanese place; call here/rtk_bases_near for detail.' This gives a clear trigger condition and names the alternatives for follow-up, so an agent knows when to invoke this tool versus its siblings.

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

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

There are no annotations, so the description carries the burden. It does disclose that the tool returns titles, one-line summaries, and URLs)SkipL, which signals a read-only index behavior. But it does not explain what the lang parameter does, whether ja changes the content language, or any other behavioral caveats.

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?

One compact sentence front-loads the resource and purpose, uses a list to convey scope, and ends with the precise return format. Every phrase contributes useful 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?

For a simple one-parameter index tool, the description covers purpose, content, and return shape. The main gap is the unexplained lang parameter, but the default and enum values make the tool usable without further detail.

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 0%, yet the single parameter is an optional enum with 'en', 'ja', and a default of 'en', so the schema largely documents itself. The description does not add any meaning to the lang parameter, which would have been helpful.

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 states a clear resource ('Index of the English field guide for engineers using GNSS in Japan') and lists concrete topics plus the return payload (titles, one-line summaries, URLs). It is specific enough to distinguish itself from tools like rtk_bases_near, though it does not explicitly contrast itself with any sibling.

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?

Usage context is implied by the topic list and the word 'Index' – an agent can infer that this is for retrieving guide entries rather than, say, nearby base stations. However, there is no explicit when-to-use guidance or mention of alternatives such as rtk_bases_near.

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

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

With no annotations, the description carries the transparency burden. It usefully discloses that warnings come from JMA and are relayed, not issued, and that prices originate from kurashigram.com. It also states equivalence to a GET endpoint, implying read-only behavior. It does not mention possible errors, data freshness, or output format, but it does provide meaningful behavioral context.

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 dense and front-loaded with 'For one point in Japan,' but it is a very long run-on sentence that repeats list-like content already partially available in the schema's sections enumeration. It contains useful added detail, yet it could be restructured into a scannable list without losing 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?

For a complex multi-section tool with no output schema, the description gives a broad inventory of returned data and cites the source endpoint. It is sufficient for a basic call, but it does not describe response shape, units for WBGT/elevation, or how warnings are structured. The description names the pieces but leaves the exact payload contract unspecified.

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 75%, so the schema already documents lat/lon ranges, lang, and the sections value list. The description adds meaning by enumerating what each section actually contains (municipality code, JMA warning names, hazard zones, furusato-nozei prices, etc.), which helps the agent construct a useful sections parameter.

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 identifies a point-in-Japan location bundle: municipality, warnings, AMeDAS, WBGT, earthquakes, hazards, elevation, RTK, and prices. It names the exact resource and a canonical GET endpoint, which makes the tool's purpose specific. However, it does not explicitly distinguish this tool from siblings like place, kurashigram_prices, or rtk_bases_near, even though it overlaps with them.

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?

There is no guidance about when to call this aggregated tool versus a more specific sibling such as kurashigram_prices or rtk_bases_near. The phrase 'For one point in Japan' implies a spatial query, but no when-to-use or when-not-to-use conditions are stated.

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

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

No annotations are provided, so the description carries the full burden. It usefully discloses the data provenance (kurashigram.com, CC BY 4.0) and the normalization applied, but it does not describe runtime behaviors such as network dependence, empty results, invalid-code handling, or output format. This is adequate but leaves meaningful gaps.

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 a single, dense sentence that front-loads the core purpose, then adds normalization, source, license, and keying information with no wasted words.

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?

For a simple one-parameter lookup, the description is largely complete: it explains what data is returned (return goods), how it is normalized, and where it comes from. It lacks an explicit output schema and error behavior, but the low complexity makes the likely return shape inferable. This is slightly above minimal viability.

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 input schema already provides 100% parameter coverage with '5-digit municipality code', so the baseline is 3. The description adds that the data is keyed by this code, but this is only marginal context beyond what the schema already states.

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 lists Furusato-nozei return goods for a Japanese municipality on Rakuten, with a specific normalization (grams per 10,000 yen). It names the data source and license, and the purpose is distinct from the unrelated sibling tools (GNSS, place, RTK bases).

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 intended use is implied: provide a 5-digit municipality code to get normalized return-good data. However, there is no explicit statement about when to use this tool versus alternatives, nor any exclusions or conditions. The siblings are unrelated, but the description leaves selection guidance to inference.

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

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

With no annotations provided, the description carries the full burden and succeeds unusually well. It discloses the live-vs-timetable data caveat ('Live positions only where operators publish them; boats are timetables'), the no-key requirement, the coverage limits, and the exact shape of the return payload — exactly the behavioral traits an agent needs that the schema cannot convey.

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?

Roughly 130 words, but every clause earns its place: audience first, then query format with examples, output contract, coverage boundary, and caveats. The return specification is grouped logically (boat vs bus/tram/monorail) and the 'No key' note resolves the most common agent question in two words.

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 there is no output schema, the description functions as the entire return contract, enumerating per-mode fields and both URLs. It also covers the three things an agent cannot infer from schema or annotations — geographic scope, data freshness behavior, and authentication — making it complete enough to invoke correctly on a first try.

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 only 50%, so the description must compensate — and it does for 'q' by supplying six varied examples (Miyajima, 宮島, Naoshima ferry, Shirakawa-go bus) and clarifying that mode words like next/ferry/bus are acceptable. The 'lang' parameter is not addressed in the description, but its enum values and default are self-documenting in the schema, so the gap is minor.

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 uses a specific verb ('Returns') with a clearly named resource — next boat/bus/tram/monorail departures from a place in Japan — and enumerates the exact output fields (time, minutes to go, operator, route, stop number, URLs). The scope is sharply defined by region list and place count, which is enough to distinguish it from siblings like 'place' or 'rtk_bases_near' even without explicitly naming them.

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?

Clear context is set: 'For travellers in Japan', with concrete query examples and an explicit coverage boundary (six regions, 26 places, validity through 2026-09) that lets an agent infer whether a location is supported. It stops short of naming alternatives or stating when-not-to-use, but the audience and scope are specific enough to route selection correctly.

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

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

With no annotations, the description carries the transparency burden. It discloses that data are static, pins the census year to 2020, names the JMA source, and routes live queries to here. It does not discuss auth or rate limits, but for a read-only fact lookup this is reasonable.

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 one dense, well-ordered sentence: purpose, parameter standard, returned fields, then sibling routing. No wasted words and the most important information is front-loaded.

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?

For a single-parameter lookup with no output schema, the description lists the expected return fields and the alternative for live data. It leaves the exact representation of the centre point implicit, but an agent still has enough to select and call the tool 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 coverage is 100%, so the schema already documents the code parameter fully. The description adds the JIS X 0402 standard in passing, but this is marginal beyond the schema's own '5-digit municipality code' example.

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 opens with a precise resource statement: 'Static facts for a municipality of Japan by 5-digit code' and then enumerates the exact returned facts. It also differentiates from the sibling here by explicitly telling the agent to use here for live values.

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 explicitly says 'Use the here tool with the centre point for live values,' which tells the agent when to prefer a sibling tool. The word 'Static' in the first sentence establishes the matching use case for place, so the when/when-not guidance is clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool returns mountpoint, host, distance in km, and baseline grade, and mentions the data source (KUON GEO's 10-minute polling). It doesn't disclose limitations like result count, accuracy, or error behavior, but the read-only nature is implicit. This is adequate but not rich.

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 a single, front-loaded sentence that states the core function immediately. It packs useful details (worldwide, NTRIP, data source, return fields) without unnecessary words. Minor deduction for dense phrasing that could be slightly clearer on parameter meaning.

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?

For a simple two-parameter tool with no output schema, the description covers the main purpose and output fields. However, it omits practical details like result ordering, maximum number of bases, or any constraints (e.g., distance limits). Given the tool's simplicity, this is a moderate gap, not a critical one.

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 0%, so the description must explain lat and lon. It only implies they represent a point ('to a point') but doesn't specify coordinate format, units, or required ranges. This is a significant gap for a tool with only two parameters, leaving the agent to guess at standard lat/lon conventions.

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 finds the nearest online community RTK base stations (NTRIP) to a point, worldwide. It specifies the resource and the operation ('nearest'), which is unambiguous. While it doesn't explicitly differentiate from siblings like gnss_in_japan, the domain is distinct enough that an agent can infer its purpose without confusion.

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?

There is no guidance on when to use this tool versus alternatives. No exclusions, prerequisites, or conditions are mentioned. The description implies use when you need nearby RTK bases, but it doesn't address other contexts or mention sibling tools that might be more appropriate for different queries.

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