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SearchAPI MCP Server

Name: SearchAPI MCP Server
Author: mrgoonie

by mrgoonie

Overview Schema Related Servers Score Discussions

TypeScript

Hybrid

SearchAPI.site - MCP サーバー

このプロジェクトは、 SearchAPI.siteを介して AI アシスタントを外部データソース (Google、Bing など) に接続するモデルコンテキストプロトコル (MCP) サーバーを提供します。

利用可能なプラットフォーム

[x] Google - ウェブ検索
[x] Google - 画像検索
[x] Google - YouTube検索
[ ] Googleマップ検索
[x] Bing - ウェブ検索
[ ] Bing - 画像検索
[ ] レディット
[ ] X/ツイッター
[ ] Facebook検索
[ ] Facebookグループ検索
[ ] インスタグラム
[ ] ティックトック

SearchAPI.site

Related MCP server: WebSearch-MCP

サポートされているトランスポート

[x] "stdio"トランスポート - CLI 使用時のデフォルトのトランスポート
[x]「ストリーミング可能なHTTP」トランスポート - Webベースのクライアント向け
- [ ] 認証を実装する（ Bearer <token>を使用した「Authorization」ヘッダー）
[ ] ~~「sse」輸送~~(非推奨)
[ ] テストを書く

使い方

コマンドライン

# Google search via CLI
npm run dev:cli -- search-google --query "your search query" --api-key "your-api-key"

# Google image search via CLI
npm run dev:cli -- search-google-images --query "your search query" --api-key "your-api-key"

# YouTube search via CLI
npm run dev:cli -- search-youtube --query "your search query" --api-key "your-api-key" --max-results 5

MCPセットアップ

stdio トランスポートを使用したローカル構成の場合:

{
  "mcpServers": {
    "searchapi": {
      "command": "node",
      "args": ["/path/to/searchapi-mcp-server/dist/index.js"],
      "transportType": "stdio"
    }
  }
}

リモート HTTP 構成の場合:

{
  "mcpServers": {
    "searchapi": {
      "type": "http",
      "url": "http://mcp.searchapi.site/mcp"
    }
  }
}

HTTP トランスポートの環境変数:

次の環境変数を使用して HTTP サーバーを構成できます。

MCP_HTTP_HOST : バインドするホスト（デフォルト: 127.0.0.1 ）
MCP_HTTP_PORT : リッスンするポート（デフォルト: 8080 ）
MCP_HTTP_PATH : エンドポイントパス（デフォルト: /mcp ）

ソースコードの概要

MCPとは何ですか?

モデルコンテキストプロトコル (MCP) は、AI システムが外部ツールやデータソースに安全かつコンテキストに応じて接続できるようにするオープンスタンダードです。

このボイラープレートは、任意の API またはデータソース用のカスタム MCP サーバーを構築するために拡張できる、クリーンな階層化アーキテクチャを使用して MCP 仕様を実装します。

この定型句を使用する理由

実稼働対応アーキテクチャ: 公開された MCP サーバーで使用されるのと同じパターンに従い、CLI、ツール、コントローラー、およびサービスが明確に分離されています。
型の安全性: 開発者エクスペリエンス、コード品質、保守性を向上させるために TypeScript を使用して構築されています。
動作例: CLI から API 統合までの完全なパターンを示す、完全に実装された IP 検索ツールが含まれています。
テストフレームワーク: カバレッジレポートを含む、ユニットテストと CLI 統合テストの両方のテストインフラストラクチャが付属しています。
開発ツール: ESLint、Prettier、TypeScript、および MCP サーバー開発用に事前構成されたその他の高品質ツールが含まれています。

はじめる

前提条件

Node.js (>=18.x):ダウンロード
Git : バージョン管理用

ステップ1: クローンとインストール

# Clone the repository
git clone https://github.com/mrgoonie/searchapi-mcp-server.git
cd searchapi-mcp-server

# Install dependencies
npm install

ステップ2: 開発サーバーを実行する

stdio トランスポートを使用して開発モードでサーバーを起動します (デフォルト)。

npm run dev:server

または、ストリーミング可能な HTTP トランスポートを使用する場合:

npm run dev:server:http

これにより、ホットリロードで MCP サーバーが起動し、 http://localhost:5173で MCP インスペクターが有効になります。

⚙️ プロキシサーバーはポート 6277 で待機しています 🔍 MCP Inspector はhttp://127.0.0.1:6274で稼働しています

HTTP トランスポートを使用する場合、サーバーはデフォルトでhttp://127.0.0.1:8080/mcpで利用できるようになります。

ステップ3: サンプルツールをテストする

CLI からサンプル IP 検索ツールを実行します。

# Using CLI in development mode
npm run dev:cli -- search-google --query "your search query" --api-key "your-api-key"

# Or with a specific IP
npm run dev:cli -- search-google --query "your search query" --api-key "your-api-key" --limit 10 --offset 0 --sort "date:d" --from_date "2023-01-01" --to_date "2023-12-31"

建築

この定型句は、懸念事項を分離し、保守性を促進する、クリーンな階層化アーキテクチャパターンに従います。

プロジェクト構造

src/
├── cli/              # Command-line interfaces
├── controllers/      # Business logic
├── resources/        # MCP resources: expose data and content from your servers to LLMs
├── services/         # External API interactions
├── tools/            # MCP tool definitions
├── types/            # Type definitions
├── utils/            # Shared utilities
└── index.ts          # Entry point

階層と責任

CLI レイヤー ( `src/cli/*.cli.ts` )

目的: 引数を解析してコントローラを呼び出すコマンドラインインターフェースを定義する
命名: ファイル名は<feature>.cli.tsとします
テスト: <feature>.cli.test.ts内の CLI 統合テスト

ツールレイヤー ( `src/tools/*.tool.ts` )

目的: AIアシスタント用のスキーマと説明を備えたMCPツールを定義する
命名: ファイルは<feature>.tool.tsという名前にし、型は<feature>.types.tsに記述します。
パターン: 各ツールは引数の検証に zod を使用する必要があります

コントローラーレイヤー ( `src/controllers/*.controller.ts` )

目的: ビジネスロジックを実装し、エラーを処理し、応答をフォーマットする
命名: ファイル名は<feature>.controller.tsとします
パターン: 標準化されたControllerResponseオブジェクトを返す必要があります

サービス層 ( `src/services/*.service.ts` )

目的: 外部APIまたはデータソースとのやり取り
命名: ファイル名は<feature>.service.tsとします
パターン: 最小限のロジックによる純粋な API のインタラクション

ユーティリティ層 ( `src/utils/*.util.ts` )

目的: アプリケーション全体で共有機能を提供する
主なユーティリティ:
- logger.util.ts : 構造化ログ
- error.util.ts : エラー処理と標準化
- formatter.util.ts : Markdown フォーマットヘルパー

開発ガイド

開発スクリプト

# Start server in development mode (hot-reload & inspector)
npm run dev:server

# Run CLI in development mode
npm run dev:cli -- [command] [args]

# Build the project
npm run build

# Start server in production mode
npm run start:server

# Run CLI in production mode
npm run start:cli -- [command] [args]

テスト

# Run all tests
npm test

# Run specific tests
npm test -- src/path/to/test.ts

# Generate test coverage report
npm run test:coverage

評価

evalsパッケージはmcpクライアントをロードし、index.tsファイルを実行するため、テスト間でリビルドする必要はありません。npxコマンドの先頭に環境変数をロードすることもできます。完全なドキュメントはこちらでご覧いただけます。

OPENAI_API_KEY=your-key  npx mcp-eval src/evals/evals.ts src/tools/searchapi.tool.ts

コード品質

# Lint code
npm run lint

# Format code with Prettier
npm run format

# Check types
npm run typecheck

カスタムツールの構築

独自のツールをサーバーに追加するには、次の手順に従います。

1. サービス層を定義する

外部 API と対話するためにsrc/services/に新しいサービスを作成します。

// src/services/example.service.ts
import { Logger } from '../utils/logger.util.js';

const logger = Logger.forContext('services/example.service.ts');

export async function getData(param: string): Promise<any> {
	logger.debug('Getting data', { param });
	// API interaction code here
	return { result: 'example data' };
}

2. コントローラーを作成する

ビジネスロジックを処理するために、 src/controllers/にコントローラーを追加します。

// src/controllers/example.controller.ts
import { Logger } from '../utils/logger.util.js';
import * as exampleService from '../services/example.service.js';
import { formatMarkdown } from '../utils/formatter.util.js';
import { handleControllerError } from '../utils/error-handler.util.js';
import { ControllerResponse } from '../types/common.types.js';

const logger = Logger.forContext('controllers/example.controller.ts');

export interface GetDataOptions {
	param?: string;
}

export async function getData(
	options: GetDataOptions = {},
): Promise<ControllerResponse> {
	try {
		logger.debug('Getting data with options', options);

		const data = await exampleService.getData(options.param || 'default');

		const content = formatMarkdown(data);

		return { content };
	} catch (error) {
		throw handleControllerError(error, {
			entityType: 'ExampleData',
			operation: 'getData',
			source: 'controllers/example.controller.ts',
		});
	}
}

3. MCPツールを実装する

src/tools/にツール定義を作成します。

// src/tools/example.tool.ts
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { z } from 'zod';
import { Logger } from '../utils/logger.util.js';
import { formatErrorForMcpTool } from '../utils/error.util.js';
import * as exampleController from '../controllers/example.controller.js';

const logger = Logger.forContext('tools/example.tool.ts');

const GetDataArgs = z.object({
	param: z.string().optional().describe('Optional parameter'),
});

type GetDataArgsType = z.infer<typeof GetDataArgs>;

async function handleGetData(args: GetDataArgsType) {
	try {
		logger.debug('Tool get_data called', args);

		const result = await exampleController.getData({
			param: args.param,
		});

		return {
			content: [{ type: 'text' as const, text: result.content }],
		};
	} catch (error) {
		logger.error('Tool get_data failed', error);
		return formatErrorForMcpTool(error);
	}
}

export function register(server: McpServer) {
	server.tool(
		'get_data',
		`Gets data from the example API, optionally using \`param\`.
Use this to fetch example data. Returns formatted data as Markdown.`,
		GetDataArgs.shape,
		handleGetData,
	);
}

4. CLIサポートを追加する

src/cli/に CLI コマンドを作成します。

// src/cli/example.cli.ts
import { program } from 'commander';
import { Logger } from '../utils/logger.util.js';
import * as exampleController from '../controllers/example.controller.js';
import { handleCliError } from '../utils/error-handler.util.js';

const logger = Logger.forContext('cli/example.cli.ts');

program
	.command('get-data')
	.description('Get example data')
	.option('--param <value>', 'Optional parameter')
	.action(async (options) => {
		try {
			logger.debug('CLI get-data called', options);

			const result = await exampleController.getData({
				param: options.param,
			});

			console.log(result.content);
		} catch (error) {
			handleCliError(error);
		}
	});

5. コンポーネントを登録する

新しいコンポーネントを登録するには、エントリポイントを更新します。

// In src/cli/index.ts
import '../cli/example.cli.js';

// In src/index.ts (for the tool)
import exampleTool from './tools/example.tool.js';
// Then in registerTools function:
exampleTool.register(server);

デバッグツール

MCP検査官

ビジュアル MCP インスペクターにアクセスしてツールをテストし、リクエスト/レスポンスの詳細を表示します。

npm run dev:server実行します。
ブラウザでhttp://localhost:5173を開きます。
ツールをテストし、UI で直接ログを表示します

サーバーログ

開発用のデバッグログを有効にします。

# Set environment variable
DEBUG=true npm run dev:server

# Or configure in ~/.mcp/configs.json

MCPサーバーの公開

カスタム MCP サーバーを公開する準備ができたら、次の手順を実行します。

詳細を記載したpackage.jsonを更新します
ツールのドキュメントをREADME.mdに更新する
プロジェクトをビルドします: npm run build
本番ビルドをテストする: npm run start:server
npmに公開: npm publish

ライセンス

ISCライセンス

{
	"searchapi": {
		"environments": {
			"DEBUG": "true",
			"SEARCHAPI_API_KEY": "value"
		}
	}
}

**注:**後方互換性のため、 searchapiキーが見つからない場合、サーバーは完全なパッケージ名 ( searchapi-mcp-server ) またはスコープ外のパッケージ名 ( searchapi-mcp-server ) の設定も認識します。ただし、新しい設定では短縮形のsearchapiキーを使用することをお勧めします。

Available Tools

3 tools

search_googleC

Performs a Google search using SearchAPI.site. Requires a search "query" string, can be able to search multiple keywords that separated by commas. Returns formatted search results including titles, snippets, and links.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	The search query to perform
`limit`	No	Maximum number of results to return (1-100)
`offset`	No	Offset for pagination
`sort`	No	Sort order (e.g., "date:d" for newest first)
`from_date`	No	Start date for filtering results (format: YYYY-MM-DD)
`to_date`	No	End date for filtering results (format: YYYY-MM-DD)

TDQS

C2.8/5.0

Behavior2/5

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 states the tool 'Returns formatted search results including titles, snippets, and links,' which gives some output context, but lacks critical behavioral details like rate limits, authentication requirements, error handling, pagination behavior (beyond the offset parameter), or whether this is a read-only operation. The mention of 'SearchAPI.site' hints at a third-party service but doesn't explain implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise with three sentences, but it's not optimally front-loaded. The first sentence states the purpose, but the second sentence awkwardly mixes parameter guidance ('Requires a search "query" string') with feature description ('can be able to search multiple keywords'). The third sentence covers return values. Some redundancy exists (e.g., 'query' is mentioned twice), and the structure could be tighter for better clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and 6 parameters (though well-documented in schema), the description is incomplete. It lacks behavioral context (e.g., rate limits, auth), doesn't explain the relationship with sibling tools, and provides minimal guidance on usage. For a search tool with multiple parameters and no structured output definition, more contextual information would be helpful for an AI agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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 6 parameters thoroughly. The description adds minimal value beyond the schema: it mentions the query parameter and that it 'can be able to search multiple keywords that separated by commas' (though awkwardly phrased), but doesn't explain other parameters like limit, offset, sort, from_date, or to_date. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Performs a Google search using SearchAPI.site' with a specific verb ('Performs') and resource ('Google search'), distinguishing it from sibling tools like search_google_images and search_youtube by focusing on general web search. However, it doesn't explicitly contrast with siblings beyond the different search types.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 like search_google_images or search_youtube. It mentions the tool can search multiple keywords separated by commas, but this is more about parameter usage than contextual guidance. No explicit when/when-not instructions or alternative recommendations are included.

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

search_google_imagesB

Performs a Google image search using SearchAPI.site. Requires a search query and your SearchAPI.site API key. Returns formatted image search results including titles, thumbnails, and source links.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	The image search query to perform
`limit`	No	Maximum number of results to return (1-100)
`offset`	No	Offset for pagination
`sort`	No	Sort order (e.g., "date:d" for newest first)
`from_date`	No	Start date for filtering results (format: YYYY-MM-DD)
`to_date`	No	End date for filtering results (format: YYYY-MM-DD)

TDQS

B3.2/5.0

Behavior3/5

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 mentions the API key requirement (authentication need) and describes the return format ('formatted image search results including titles, thumbnails, and source links'), which adds value beyond the input schema. However, it doesn't mention rate limits, error conditions, or other behavioral traits like whether results are cached or real-time.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with three concise sentences that each add value: what it does, what it requires, and what it returns. It's front-loaded with the core purpose. There's minimal waste, though it could be slightly more structured with bullet points for the three key pieces of information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 6 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate but incomplete context. It covers the purpose, authentication requirement, and return format, but doesn't address error handling, rate limits, or provide examples. The absence of an output schema means the description's mention of return format is helpful but could be more detailed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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 6 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it mentions 'search query' and 'API key' but doesn't explain parameter interactions, defaults, or usage examples. Baseline 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'performs a Google image search using SearchAPI.site' which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'search_google' and 'search_youtube' by specifying it's for images, though it doesn't explicitly contrast with them in the description text itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions the requirement for a SearchAPI.site API key, which provides some usage context. However, it offers no guidance on when to use this tool versus the sibling tools (search_google, search_youtube) or any alternatives. There's no explicit 'when' or 'when not' guidance.

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

search_youtubeB

Performs a YouTube search using SearchAPI.site. Requires a search query and your SearchAPI.site API key. Returns formatted YouTube search results including video titles, thumbnails, descriptions, and links. Supports optional parameters for pagination, sorting, filtering by date and duration.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	The YouTube search query to perform
`maxResults`	No	Maximum number of results to return (1-50)
`pageToken`	No	Token for pagination to get next/previous page of results
`order`	No	Sort order for results
`publishedAfter`	No	Number of days to filter videos from
`videoDuration`	No	Filter by video duration

TDQS

B3.4/5.0

Behavior3/5

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 mentions the API key requirement (auth needs) and describes the return format (video titles, thumbnails, descriptions, links), which adds value beyond the input schema. However, it doesn't cover rate limits, error handling, or other operational constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with four sentences that each add value: purpose, requirements, returns, and optional features. It's front-loaded with core functionality. Minor improvement could come from tighter phrasing, but there's no wasted content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with 6 parameters, 100% schema coverage, and no output schema, the description provides adequate context on what the tool does and returns. However, without annotations or output schema, it lacks details on response structure, error cases, or performance characteristics that would help an agent use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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 6 parameters thoroughly. The description adds minimal value by listing optional parameters (pagination, sorting, filtering by date and duration) but doesn't provide additional syntax, format, or usage details beyond what's in the schema. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Performs a YouTube search') and resource ('using SearchAPI.site'), distinguishing it from sibling tools like search_google and search_google_images by specifying YouTube as the search target. It provides a complete verb+resource+scope combination.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions when to use this tool (for YouTube searches) but provides no guidance on when to choose it versus the sibling tools search_google or search_google_images. There's no explicit comparison or exclusion criteria, leaving the agent to infer usage context.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

3 tool updatesAug 14, 2025
- First observedsearch_google
- First observedsearch_google_images
- First observedsearch_youtube

Install Server

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TDQS

B3.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose targeting a specific search type: Google web search, Google image search, and YouTube search. The descriptions explicitly differentiate them by platform and result format, with no overlap in functionality that could cause confusion.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with 'search_' prefix followed by the target platform (google, google_images, youtube). This predictable naming scheme makes it easy for agents to understand and select the appropriate tool.

Tool Count4/5

Three tools is reasonable for a search API server, covering major search platforms. However, it feels slightly thin—adding tools for other platforms (like news or shopping search) could make it more comprehensive, but the current count is appropriate for the core functionality.

Completeness4/5

The toolset covers the essential search operations for Google web, images, and YouTube, which aligns well with the server's purpose. A minor gap is the lack of a general search tool that could handle other platforms or unified search, but agents can work effectively with the provided tools.

Maintenance

ActivityInactive

ResponsivenessUnresponsive

Resources

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MCP directory API

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SearchAPI.site - MCP サーバー

利用可能なプラットフォーム

SearchAPI.site

サポートされているトランスポート

使い方

コマンドライン

MCPセットアップ

ソースコードの概要

MCPとは何ですか?

この定型句を使用する理由

はじめる

前提条件

ステップ1: クローンとインストール

ステップ2: 開発サーバーを実行する

ステップ3: サンプルツールをテストする

建築

プロジェクト構造

階層と責任

CLI レイヤー ( src/cli/*.cli.ts )

ツールレイヤー ( src/tools/*.tool.ts )

コントローラーレイヤー ( src/controllers/*.controller.ts )

サービス層 ( src/services/*.service.ts )

ユーティリティ層 ( src/utils/*.util.ts )

開発ガイド

開発スクリプト

テスト

評価

コード品質

カスタムツールの構築

1. サービス層を定義する

2. コントローラーを作成する

3. MCPツールを実装する

4. CLIサポートを追加する

5. コンポーネントを登録する

デバッグツール

MCP検査官

サーバーログ

MCPサーバーの公開

ライセンス

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

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Appeared in Searches

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MCP directory API

CLI レイヤー ( `src/cli/*.cli.ts` )

ツールレイヤー ( `src/tools/*.tool.ts` )

コントローラーレイヤー ( `src/controllers/*.controller.ts` )

サービス層 ( `src/services/*.service.ts` )

ユーティリティ層 ( `src/utils/*.util.ts` )