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SearchAPI MCP Server

by mrgoonie
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

SearchAPI.site - MCP-Server

Dieses Projekt stellt einen Model Context Protocol (MCP)-Server bereit, der KI-Assistenten über SearchAPI.site mit externen Datenquellen (Google, Bing usw.) verbindet.

Verfügbare Plattformen

  • [x] Google - Websuche

  • [x] Google - Bildersuche

  • [x] Google - YouTube-Suche

  • [ ] Google - Maps Suche

  • [x] Bing - Websuche

  • [ ] Bing - Bildersuche

  • [ ] Reddit

  • [ ] X/Twitter

  • [ ] Facebook-Suche

  • [ ] Facebook-Gruppensuche

  • [ ] Instagram

  • [ ] TikTok

SearchAPI.site

Related MCP server: WebSearch-MCP

Unterstützte Transporte

  • [x] "stdio" -Transport - Standardtransport für die CLI-Nutzung

  • [x]"Streamable HTTP" -Transport - Für webbasierte Clients

    • [ ] Implementieren Sie Auth ("Authorization"-Header mit Bearer <token> )

  • [ ] "sse"-Transport (Veraltet)

  • [ ] Tests schreiben

Anwendung

Befehlszeilenschnittstelle (CLI)

# 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-Einrichtung

Für die lokale Konfiguration mit stdio-Transport:

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

Für die Remote-HTTP-Konfiguration:

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

Umgebungsvariablen für den HTTP-Transport:

Sie können den HTTP-Server mit diesen Umgebungsvariablen konfigurieren:

  • MCP_HTTP_HOST : Der Host, an den die Verbindung hergestellt werden soll (Standard: 127.0.0.1 )

  • MCP_HTTP_PORT : Der Port, auf dem gewartet werden soll (Standard: 8080 )

  • MCP_HTTP_PATH : Der Endpunktpfad (Standard: /mcp )

Quellcodeübersicht

Was ist MCP?

Model Context Protocol (MCP) ist ein offener Standard, der es KI-Systemen ermöglicht, sich sicher und kontextbezogen mit externen Tools und Datenquellen zu verbinden.

Dieses Boilerplate implementiert die MCP-Spezifikation mit einer sauberen, geschichteten Architektur, die erweitert werden kann, um benutzerdefinierte MCP-Server für jede API oder Datenquelle zu erstellen.

Warum diesen Standardtext verwenden?

  • Produktionsreife Architektur : Folgt dem gleichen Muster wie veröffentlichte MCP-Server, mit klarer Trennung zwischen CLI, Tools, Controllern und Diensten.

  • Typsicherheit : Erstellt mit TypeScript für verbesserte Entwicklererfahrung, Codequalität und Wartbarkeit.

  • Funktionierendes Beispiel : Enthält ein vollständig implementiertes IP-Lookup-Tool, das das komplette Muster von der CLI- bis zur API-Integration demonstriert.

  • Testframework : Wird mit einer Testinfrastruktur für Unit- und CLI-Integrationstests geliefert, einschließlich Abdeckungsberichten.

  • Entwicklungstools : Enthält ESLint, Prettier, TypeScript und andere hochwertige Tools, die für die MCP-Serverentwicklung vorkonfiguriert sind.

Erste Schritte

Voraussetzungen

Schritt 1: Klonen und installieren

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

# Install dependencies
npm install

Schritt 2: Entwicklungsserver ausführen

Starten Sie den Server im Entwicklungsmodus mit stdio-Transport (Standard):

npm run dev:server

Oder mit dem Streamable HTTP-Transport:

npm run dev:server:http

Dadurch wird der MCP-Server mit Hot-Reloading gestartet und der MCP Inspector unter http://localhost:5173 aktiviert.

⚙️ Proxyserver lauscht auf Port 6277 🔍 MCP Inspector ist unter http://127.0.0.1:6274 aktiv

Bei Verwendung des HTTP-Transports ist der Server standardmäßig unter http://127.0.0.1:8080/mcp verfügbar.

Schritt 3: Testen Sie das Beispieltool

Führen Sie das Beispiel-IP-Lookup-Tool über die CLI aus:

# 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"

Architektur

Dieser Standardtext folgt einem klaren, geschichteten Architekturmuster, das Belange trennt und die Wartbarkeit fördert.

Projektstruktur

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

Ebenen und Verantwortlichkeiten

CLI-Schicht ( src/cli/*.cli.ts )

  • Zweck : Definieren Sie Befehlszeilenschnittstellen, die Argumente analysieren und Controller aufrufen

  • Benennung : Dateien sollten <feature>.cli.ts heißen

  • Testen : CLI-Integrationstests in <feature>.cli.test.ts

Tools-Ebene ( src/tools/*.tool.ts )

  • Zweck : Definieren Sie MCP-Tools mit Schemata und Beschreibungen für KI-Assistenten

  • Benennung : Dateien sollten <feature>.tool.ts mit Typen in <feature>.types.ts benannt werden

  • Muster : Jedes Tool sollte zod zur Argumentvalidierung verwenden

Controller-Ebene ( src/controllers/*.controller.ts )

  • Zweck : Implementieren Sie Geschäftslogik, behandeln Sie Fehler und formatieren Sie Antworten

  • Benennung : Dateien sollten <feature>.controller.ts heißen

  • Muster : Sollte standardisierte ControllerResponse Objekte zurückgeben

Serviceebene ( src/services/*.service.ts )

  • Zweck : Interaktion mit externen APIs oder Datenquellen

  • Benennung : Dateien sollten <feature>.service.ts heißen

  • Muster : Reine API-Interaktionen mit minimaler Logik

Utils-Ebene ( src/utils/*.util.ts )

  • Zweck : Bereitstellung gemeinsamer Funktionen für die gesamte Anwendung

  • Wichtige Dienstprogramme :

    • logger.util.ts : Strukturiertes Protokollieren

    • error.util.ts : Fehlerbehandlung und Standardisierung

    • formatter.util.ts : Markdown-Formatierungshilfen

Entwicklungshandbuch

Entwicklungsskripte

# 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]

Testen

# Run all tests
npm test

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

# Generate test coverage report
npm run test:coverage

Bewertungen

Das Evals-Paket lädt einen MCP-Client, der anschließend die Datei index.ts ausführt, sodass zwischen den Tests kein Neuaufbau erforderlich ist. Sie können Umgebungsvariablen laden, indem Sie dem Befehl npx voranstellen. Die vollständige Dokumentation finden Sie hier .

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

Codequalität

# Lint code
npm run lint

# Format code with Prettier
npm run format

# Check types
npm run typecheck

Erstellen benutzerdefinierter Tools

Befolgen Sie diese Schritte, um dem Server Ihre eigenen Tools hinzuzufügen:

1. Service-Layer definieren

Erstellen Sie einen neuen Dienst in src/services/ um mit Ihrer externen API zu interagieren:

// 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. Controller erstellen

Fügen Sie einen Controller in src/controllers/ hinzu, um die Geschäftslogik zu handhaben:

// 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. Implementieren Sie das MCP-Tool

Erstellen Sie eine Tooldefinition in src/tools/ :

// 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-Unterstützung hinzufügen

Erstellen Sie einen CLI-Befehl in src/cli/ :

// 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. Komponenten registrieren

Aktualisieren Sie die Einstiegspunkte, um Ihre neuen Komponenten zu registrieren:

// 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);

Debugging-Tools

MCP-Inspektor

Greifen Sie auf den visuellen MCP-Inspektor zu, um Ihre Tools zu testen und Anforderungs-/Antwortdetails anzuzeigen:

  1. Führen Sie npm run dev:server

  2. Öffnen Sie http://localhost:5173 in Ihrem Browser

  3. Testen Sie Ihre Tools und zeigen Sie Protokolle direkt in der Benutzeroberfläche an

Serverprotokolle

Aktivieren Sie Debug-Protokolle für die Entwicklung:

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

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

Veröffentlichen Ihres MCP-Servers

Wenn Sie bereit sind, Ihren benutzerdefinierten MCP-Server zu veröffentlichen:

  1. Aktualisieren Sie package.json mit Ihren Details

  2. Aktualisieren Sie README.md mit Ihrer Tool-Dokumentation

  3. Erstellen Sie das Projekt: npm run build

  4. Testen Sie den Produktionsbuild: npm run start:server

  5. Auf npm veröffentlichen: npm publish

Lizenz

ISC-Lizenz

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

Hinweis: Aus Gründen der Abwärtskompatibilität erkennt der Server auch Konfigurationen mit dem vollständigen Paketnamen ( searchapi-mcp-server ) oder dem Paketnamen ohne Gültigkeitsbereich ( searchapi-mcp-server ), wenn der searchapi Schlüssel nicht gefunden wird. Für neue Konfigurationen wird jedoch die Verwendung des kurzen searchapi -Schlüssels empfohlen.

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
NameRequiredDescriptionDefault
queryYesThe search query to perform
limitNoMaximum number of results to return (1-100)
offsetNoOffset for pagination
sortNoSort order (e.g., "date:d" for newest first)
from_dateNoStart date for filtering results (format: YYYY-MM-DD)
to_dateNoEnd 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
NameRequiredDescriptionDefault
queryYesThe image search query to perform
limitNoMaximum number of results to return (1-100)
offsetNoOffset for pagination
sortNoSort order (e.g., "date:d" for newest first)
from_dateNoStart date for filtering results (format: YYYY-MM-DD)
to_dateNoEnd 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
NameRequiredDescriptionDefault
queryYesThe YouTube search query to perform
maxResultsNoMaximum number of results to return (1-50)
pageTokenNoToken for pagination to get next/previous page of results
orderNoSort order for results
publishedAfterNoNumber of days to filter videos from
videoDurationNoFilter 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.

  1. 3 tool updates
    • 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

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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