Japan disability welfare (障害福祉) reference: service fees, add-ons, staffing. In Japanese.
Scored across 3 tools
Each tool has a clearly distinct role: search_articles finds articles, get_article retrieves full text by slug, and locate_service_code finds where a code lives in the official table. The descriptions even explicitly redirect users between tools (e.g. '加算の解説が必要なら search_articles を使うこと'), making misselection unlikely.
All three names follow a consistent snake_case verb_noun pattern (get_article, search_articles, locate_service_code). The semantics of each verb accurately reflect the operation.
Three tools is on the lean side, but for a search-and-retrieve dictionary plus a single targeted lookup they are well-scoped and each earns its place. No redundancy or filler.
The core lifecycle of the domain (search → retrieve full article → locate code provenance) is covered. Minor gaps exist, such as no way to browse or list articles/code ranges directly, but search acts as a reasonable workaround and the omission of code semantics appears intentional.
fukushi.ai の公開記事を slug で指定して全文（Markdown）を取得する。search_articles の抜粋では手順や表の全体が掴めないときに使う。本文末の脚注に、その記事が根拠にした一次情報の出典が示されている。
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | 記事のslug。search_articles の結果に含まれる。例: 「b-type-opening-guide」 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: the returned content is Markdown, and the footnotes contain primary-source citations. However, it doesn't mention potential absence of the article, error behavior, or whether the full text is always available. With annotations covering safety, a 3 is appropriate.
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?
Three sentences, each earning its place: the first states the core function, the second gives usage guidance, and the third adds a useful behavioral detail about footnotes. 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 read-only tool with one parameter, the description is nearly complete. It covers what the tool does, when to use it, and what the output contains. The only minor gap is not describing behavior when the slug doesn't exist, but the annotations and schema already cover the essential context.
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%, so the schema already documents the slug parameter. The description adds the example 'b-type-opening-guide' and notes that the slug comes from search_articles results, which is helpful but not extensive. Baseline 3 is correct when the schema does the heavy lifting.
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 specific verb ('取得する'), a specific resource ('fukushi.ai の公開記事'), and the retrieval key ('slug で指定して全文（Markdown）'). It also explicitly distinguishes itself from the sibling tool search_articles, making the tool's purpose unmistakable.
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 names the alternative (search_articles) and gives the condition for choosing this tool: when the excerpt from search_articles is insufficient to grasp procedures or tables. This is clear when-to-use guidance with a named alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
障害福祉サービスの6桁のサービスコード（例: 465140）が、厚生労働省の「介護給付費等単位数サービスコード」（コード表）のどこに載っているかを返す。返すのは所在だけ（施行の版・分冊・シート名・行番号と、厚労省のExcel・掲載ページのURL）で、コードの略称・算定項目・単位数は返さない。請求ソフトや国保連の返戻通知に出たコードの意味を確かめたいときに、原典の該当箇所へ利用者を案内するために使う。加算の要件や単位数の解説が必要なら search_articles を使うこと。
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | 6桁のサービスコード（種類2桁＋項目4桁）。例: 465140。分割形「46 5140」でもよい |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent/non-destructive/openWorld=false, so the safety profile is covered. The description goes further by specifying what is NOT returned (abbreviations, calculation items, unit counts) and enumerating what the response contains (edition, volume, sheet name, row number, MHLW Excel and page URLs). It does not mention behavior when a code is absent or unparseable, keeping it short of a 5.
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?
Front-loaded with the core action and result, then scope limits and the alternative. Dense but every clause carries information; the only mild cost is the repetition of the 6-digit-code framing already in the schema.
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?
There is no output schema, so the description must describe the return payload, and it does so concretely (施行の版・分冊・シート名・行番号 plus Excel/page URLs) while warning that code meanings are excluded. For a single-parameter lookup tool this is sufficient for correct invocation.
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% and the single parameter already documents the 6-digit format, the 種類2桁＋項目4桁 breakdown, the example 465140, and the spaced form. The description merely repeats the code format, adding no syntax or normalization detail beyond the schema, so the baseline 3 applies.
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?
States a precise verb and resource: return where a 6-digit disability-welfare service code appears in the MHLW code table. It further bounds the output (location only: edition, volume, sheet, row, URLs) and explicitly distinguishes itself from search_articles, so an agent can separate it from its siblings without opening any schema.
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?
Names the triggering situation (verifying a code seen in billing software or a Kokuho-ren return notice) and gives an explicit alternative plus the condition that selects it (use search_articles for addition requirements or unit-count explanations). When-to-use, when-not-to-use and the alternative are all present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
障害福祉制度の解説サイト「障害福祉AI辞典」(fukushi.ai) の記事を検索し、該当箇所の抜粋と記事URLを返す。記事は厚生労働省の一次情報（報酬告示・留意事項通知・指定基準省令・サービスコード表）に接地して検証済みで、本文には出典が脚注として示されている。障害者総合支援法にもとづくサービスの制度・報酬・加算・指定基準・実務手順を問われたら、まずこれを使うこと。例:「就労継続支援B型の欠席時対応加算は何単位？」「A型の開業に必要な人員基準は？」「処遇改善加算の算定要件」。扱う範囲は記事がある領域に限られる（就労系と全サービス共通が厚い）。該当が無ければ推測せず、資料に無い旨を利用者に伝えること。
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | 日本語の検索クエリ。サービス種別＋知りたい観点を具体的に並べる。例:「就労継続支援B型 欠席時対応加算 単位数」 | |
| top_k | No | 検索するチャンク数（既定 5、最大 10）。同じ記事の複数箇所が当たると1件にまとめるため、返る記事数はこれより少なくなることがある。 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds substantial context: it returns excerpts and URLs, merges multiple hits from the same article into one, and has a fallback behavior of stating absence instead of guessing. No contradiction with annotations.
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 well-structured: purpose first, then usage examples, then limitations. While somewhat long, every sentence adds value and it is not redundant. Front-loading is effective.
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?
With no output schema, the description adequately explains the return (excerpts and article URLs) and covers edge cases (no results, merging behavior). For a search tool with two params, this is complete and anticipates agent questions.
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%, but the description adds meaningful guidance: query format examples (service type + specific aspect) and clarifies that top_k is a chunk count that may result in fewer articles due to merging. This goes beyond the schema's basic parameter descriptions.
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 searches articles on a specific site (fukushi.ai), returns excerpts and URLs, and is verified against primary sources. It distinguishes itself from the sibling get_article by focusing on search rather than retrieval, and gives specific examples of queries.
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 instructs when to use this tool: when asked about services' systems, remuneration, add-ons, standards, or practical procedures, and provides concrete example questions. It also states when not to use it (if no article exists, tell the user rather than guessing) and notes scope limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
locate_service_codeget_articlesearch_articlesJapan payroll & social insurance. 日本の給与計算・社会保険。47都道府県の料率、源泉所得税、割増賃金、有給、標準報酬月額の改定、最低賃金。根拠の条文つき。
Japan minimum wage by prefecture, 協会けんぽ rates, pensions, benefits, holidays, tax figures, sourced.
Japan home-visit nursing reimbursement rules, each with its source and that source's standing.
MHLW list of medical institutions in Japan accepting foreign patients, by language. Tracks changes.
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