Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Hourei MCP Server search for laws related to personal information protection"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
e-Gov法令APIを使用して日本の法令情報を検索・取得するMCPサーバーです。
このMCPサーバーは以下のツールを提供します：
法令名や法令番号で法令を検索します。
パラメータ:
keyword (必須): 検索キーワード（法令名の一部や法令番号）
category (オプション): 法令の種別
1: 法律
2: 政令
3: 省令
4: 規則
5: その他
limit (オプション): 取得する最大件数（デフォルト: 100）
法令番号を指定して法令の詳細データを取得します。
パラメータ:
lawNum (必須): 法令番号（例: 平成十七年法律第百十七号）
法令の改正履歴を取得します。
パラメータ:
lawNum (必須): 法令番号
Related MCP server: labor-law-mcp
npx hourei-mcp-servergit clone <repository-url>
cd hourei-mcp-server
npm installClaude Desktopなどのクライアントの設定ファイル（~/Library/Application Support/Claude/claude_desktop_config.json）に以下を追加：
{
"mcpServers": {
"hourei": {
"command": "npx",
"args": ["-y", "hourei-mcp-server"]
}
}
}設定後、Claude Desktopを再起動してください。
Cursorの設定ファイル（.cursor/mcp_config.json または設定画面）に以下を追加：
{
"mcpServers": {
"hourei": {
"command": "npx",
"args": ["-y", "hourei-mcp-server"]
}
}
}ChatGPTでは、MCPサーバーのURLを指定する形式で設定します。
{
"mcpServers": {
"hourei": {
"command": "npx",
"args": ["-y", "hourei-mcp-server"]
}
}
}SSE対応サーバーを別途立ち上げた場合：
{
"mcpServers": {
"hourei": {
"url": "http://localhost:3000/sse"
}
}
}または、リモートサーバーにデプロイした場合：
{
"mcpServers": {
"hourei": {
"url": "https://your-server.com/sse"
}
}
}注意: 現在のバージョン（v1.0.5）はstdio接続のみ対応しています。URL形式で使用する場合は、別途SSE対応のラッパーサーバーが必要です。
Manusの設定ファイルに以下を追加：
{
"mcpServers": {
"hourei": {
"command": "npx",
"args": ["-y", "hourei-mcp-server"]
}
}
}DifyのエージェントでMCPツールを使用する場合：
Difyの設定ファイルに追加
環境変数またはDifyの設定で以下を指定：
{
"mcpServers": {
"hourei": {
"command": "npx",
"args": ["-y", "hourei-mcp-server"]
}
}
}エージェントブロックでの使用
Difyのワークフロー内で「ツール」ブロックを追加し、以下のMCPツールを選択：
search_law - 法令検索
get_law_data - 法令詳細取得
get_law_revision - 改正履歴取得
使用例：法令検索エージェント
入力ブロック → LLMブロック → ツールブロック(search_law) → 出力ブロックツールブロックのパラメータ設定例：
{
"keyword": "{{user_input}}",
"category": "1",
"limit": 100
}{
"mcpServers": {
"hourei": {
"command": "node",
"args": ["/path/to/hourei-mcp-server/index.js"]
}
}
}このMCPサーバーでできることの具体例です。
個人情報保護関連の法律を検索:
{
"keyword": "個人情報保護",
"category": "1" // 法律のみ
}労働基準法を検索:
{
"keyword": "労働基準法"
}消費税に関する政令を検索:
{
"keyword": "消費税",
"category": "2", // 政令
"limit": 50
}個人情報保護法の全文を取得:
{
"lawNum": "平成十五年法律第五十七号"
}民法の全文を取得:
{
"lawNum": "明治二十九年法律第八十九号"
}刑法の全文を取得:
{
"lawNum": "明治四十年法律第四十五号"
}個人情報保護法の改正履歴:
{
"lawNum": "平成十五年法律第五十七号"
}✅ 法令の検索
キーワードで法令を検索
法律、政令、省令など種別で絞り込み
最大100件まで一度に取得可能
✅ 法令データの取得
法令番号を指定して全文取得
XML形式で構造化されたデータ
条文、項、号などの階層構造を含む
✅ 改正履歴の確認
いつ、どのように法令が改正されたか
改正法令の情報を取得
施行日の確認
✅ 活用例
法的調査・リサーチ
コンプライアンスチェック
契約書作成時の参照
法令遵守の確認
法律相談の補助
法改正の追跡
Difyエージェントでの法令検索自動化
チャットボットへの法令知識統合
✅ Claude Desktop（npx経由）
✅ Cursor AI（npx経由）
✅ ChatGPT Desktop（npxまたはURL形式）
✅ Manus（npx経由）
✅ Dify（エージェントワークフロー）
✅ その他MCP対応クライアント（stdio接続対応）
Node.js ESM形式
MCP SDK v0.5.0
対応フォーマット: XML
git clone https://github.com/yourusername/hourei-mcp-server.git
cd hourei-mcp-server
npm installnode index.jsMIT
Pull Requestsを歓迎します！
問題が発生した場合は、Issuesで報告してください。
法令番号を指定して法令の詳細データを取得します。
| Name | Required | Description | Default |
|---|---|---|---|
| lawNum | Yes | 法令番号（例: 平成十七年法律第百十七号） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it states the tool retrieves data (implying read-only), it doesn't address important behavioral aspects like authentication requirements, rate limits, error conditions, or what format the 'detailed data' includes. For a tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves in practice.
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, efficient Japanese sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized for a simple retrieval tool and front-loads the essential information. Every word earns its place in conveying the core functionality.
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 lack of annotations and output schema, the description is incomplete for effective tool use. While it states what the tool does, it doesn't explain what 'detailed data' includes, potential response formats, or any behavioral constraints. For a data retrieval tool with no structured output documentation, the description should provide more context about what to expect from the tool's execution.
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 description mentions that a law number must be specified ('法令番号を指定して'), which aligns with the single required parameter 'lawNum'. However, the input schema already provides 100% coverage with a clear description and example format. The description adds minimal value beyond what's already documented in the schema, meeting the baseline for high schema coverage.
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: '法令番号を指定して法令の詳細データを取得します' (Retrieve detailed law data by specifying a law number). It specifies both the action (取得します - retrieve) and resource (法令の詳細データ - detailed law data), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like get_law_revision or search_law, which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools (get_law_revision, search_law) or explain when this specific retrieval method is appropriate versus searching or getting revisions. The agent must infer usage from the tool name and description alone, which is insufficient for optimal tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
法令の改正履歴を取得します。
| Name | Required | Description | Default |
|---|---|---|---|
| lawNum | Yes | 法令番号（例: 平成十七年法律第百十七号） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only states what the tool does ('取得します') but doesn't describe any behavioral traits: whether it's read-only or mutative, what permissions are needed, rate limits, pagination, error conditions, or response format. For a tool with zero annotation coverage, this is insufficient.
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, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a simple retrieval tool and front-loads the essential information. Every word earns its place.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what the revision history response looks like, what format it returns, or any behavioral constraints. For a tool that presumably returns structured historical data, more context about the output would be helpful to the agent.
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 description doesn't mention parameters at all. However, the input schema has 100% description coverage, with the single parameter 'lawNum' clearly documented with an example. Since schema coverage is high, the baseline score of 3 is appropriate - the description adds no parameter information beyond what the schema already provides.
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 '法令の改正履歴を取得します' clearly states the tool's purpose: retrieving revision history of laws. It uses a specific verb ('取得します' - retrieve) and resource ('法令の改正履歴' - law revision history). However, it doesn't explicitly distinguish this tool from its siblings (get_law_data, search_law), which likely retrieve different aspects of law information.
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 no guidance on when to use this tool versus its siblings (get_law_data, search_law). There's no mention of alternative tools, prerequisites, or specific contexts where this tool is preferred over others. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
法令名や法令番号で法令を検索します。キーワード検索に対応しています。
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | 検索キーワード（法令名の一部や法令番号） | |
| category | No | 法令の種別（1:法律、2:政令、3:省令など） | |
| limit | No | 取得する最大件数（デフォルト: 100） |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions keyword search functionality but doesn't describe important behaviors like pagination (only implies limit via parameter), error handling, authentication requirements, rate limits, or what the search results look like. For a search tool with zero annotation coverage, this leaves significant 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 appropriately concise with just two sentences that directly state the tool's function. It's front-loaded with the core purpose and follows with additional capability. No wasted words, though it could be slightly more structured.
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 search tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the search returns, how results are structured, whether there's pagination beyond the limit parameter, or any behavioral constraints. The description should provide more context given the lack of structured metadata.
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 all three parameters thoroughly. The description mentions keyword search but doesn't add meaningful semantic context beyond what's in the schema descriptions. The baseline of 3 is appropriate 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 clearly states the tool's purpose: searching for laws/regulations by name or number using keyword search. It specifies the resource (laws/regulations) and action (search), but doesn't explicitly differentiate from sibling tools like 'get_law_data' or 'get_law_revision' which likely retrieve specific laws rather than search broadly.
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 no guidance on when to use this tool versus the sibling tools 'get_law_data' or 'get_law_revision'. It mentions keyword search capability but doesn't specify scenarios where this is preferable to direct retrieval or other alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: get_law_data retrieves detailed data for a specific law, get_law_revision fetches amendment history, and search_law performs keyword-based searches. There is no overlap in functionality, making tool selection unambiguous.
All tools follow a consistent verb_noun naming pattern (get_law_data, get_law_revision, search_law) using snake_case. The naming is predictable and readable, with no deviations in style.
With only 3 tools, the set feels thin for a law-related server, potentially lacking operations like creating, updating, or deleting laws. However, it covers basic retrieval and search functions adequately for a minimal scope.
The tools provide search and retrieval capabilities but lack full CRUD coverage (e.g., no create, update, or delete operations). This is a notable gap for managing laws, though agents can work with the available read-only functions.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Japan Law MCP — Japanese national laws & ordinances via the e-Gov Law API.
Resolve, search and verify legal citations against the official sources, with provenance.
Search U.S. case law, fetch opinions, and ask matter-aware legal questions over your documents.
Raw Japanese regulatory data for AI agents: pension, gazette, gBizINFO. x402-metered (USDC).
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