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SearchAPI MCP Server

by mrgoonie
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TypeScript
Hybrid

SearchAPI.site - Servidor MCP

Este proyecto proporciona un servidor de Protocolo de Contexto de Modelo (MCP) que conecta a los asistentes de IA con fuentes de datos externas (Google, Bing, etc.) a través de SearchAPI.site .

Plataformas disponibles

  • [x] Google - Búsqueda web

  • [x] Google - Búsqueda de imágenes

  • [x] Google - Búsqueda en YouTube

  • [ ] Búsqueda en Google Maps

  • [x] Bing - Búsqueda web

  • [ ] Bing - Búsqueda de imágenes

  • [ ] Reddit

  • [ ] X/Twitter

  • [ ] Búsqueda en Facebook

  • [ ] Búsqueda de grupos de Facebook

  • [ ] Instagram

  • [ ] TikTok

SearchAPI.site

Related MCP server: WebSearch-MCP

Transportes soportados

  • [x] Transporte "stdio" : transporte predeterminado para el uso de CLI

  • [x] Transporte"HTTP transmitible" : para clientes web

    • [ ] Implementar autenticación (encabezados "Autorización" con Bearer <token> )

  • [ ] transporte "sse" (Obsoleto)

  • [ ] Escribir pruebas

Cómo utilizar

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

Configuración de MCP

Para la configuración local con transporte stdio:

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

Para la configuración HTTP remota:

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

Variables de entorno para el transporte HTTP:

Puede configurar el servidor HTTP utilizando estas variables de entorno:

  • MCP_HTTP_HOST : El host al que vincularse (predeterminado: 127.0.0.1 )

  • MCP_HTTP_PORT : El puerto para escuchar (predeterminado: 8080 )

  • MCP_HTTP_PATH : La ruta del punto final (predeterminado: /mcp )

Descripción general del código fuente

¿Qué es MCP?

El Protocolo de Contexto de Modelo (MCP) es un estándar abierto que permite a los sistemas de IA conectarse de forma segura y contextual con herramientas y fuentes de datos externas.

Esta plantilla implementa la especificación MCP con una arquitectura limpia y en capas que puede ampliarse para crear servidores MCP personalizados para cualquier API o fuente de datos.

¿Por qué utilizar este texto estándar?

  • Arquitectura lista para producción : sigue el mismo patrón utilizado en los servidores MCP publicados, con una clara separación entre CLI, herramientas, controladores y servicios.

  • Seguridad de tipos : creado con TypeScript para mejorar la experiencia del desarrollador, la calidad del código y la facilidad de mantenimiento.

  • Ejemplo de trabajo : incluye una herramienta de búsqueda de IP completamente implementada que demuestra el patrón completo desde la CLI hasta la integración de API.

  • Marco de pruebas : incluye infraestructura de pruebas para pruebas de integración unitarias y CLI, incluidos informes de cobertura.

  • Herramientas de desarrollo : incluye ESLint, Prettier, TypeScript y otras herramientas de calidad preconfiguradas para el desarrollo de servidores MCP.

Empezando

Prerrequisitos

  • Node.js (>=18.x): Descargar

  • Git : para el control de versiones

Paso 1: Clonar e instalar

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

# Install dependencies
npm install

Paso 2: Ejecutar el servidor de desarrollo

Inicie el servidor en modo de desarrollo con el transporte stdio (predeterminado):

npm run dev:server

O con el transporte HTTP Streamable:

npm run dev:server:http

Esto inicia el servidor MCP con recarga activa y habilita el Inspector MCP en http://localhost:5173 .

⚙️ El servidor proxy escucha en el puerto 6277 🔍 MCP Inspector está en funcionamiento en http://127.0.0.1:6274

Al utilizar el transporte HTTP, el servidor estará disponible en http://127.0.0.1:8080/mcp de forma predeterminada.

Paso 3: Pruebe la herramienta de ejemplo

Ejecute la herramienta de búsqueda de IP de ejemplo desde la CLI:

# 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"

Arquitectura

Este modelo sigue un patrón de arquitectura limpio y en capas que separa las preocupaciones y promueve la capacidad de mantenimiento.

Estructura del proyecto

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

Capas y responsabilidades

Capa CLI ( src/cli/*.cli.ts )

  • Propósito : Definir interfaces de línea de comandos que analizan argumentos y llaman a controladores.

  • Nombre : Los archivos deben llamarse <feature>.cli.ts

  • Pruebas : pruebas de integración de CLI en <feature>.cli.test.ts

Capa de herramientas ( src/tools/*.tool.ts )

  • Propósito : Definir herramientas MCP con esquemas y descripciones para asistentes de IA.

  • Nombre : Los archivos deben llamarse <feature>.tool.ts con tipos en <feature>.types.ts

  • Patrón : Cada herramienta debe usar zod para la validación de argumentos

Capa de controladores ( src/controllers/*.controller.ts )

  • Propósito : Implementar lógica empresarial, manejar errores y dar formato a respuestas.

  • Nombre : Los archivos deben llamarse <feature>.controller.ts

  • Patrón : Debe devolver objetos ControllerResponse estandarizados

Capa de servicios ( src/services/*.service.ts )

  • Propósito : Interactuar con API externas o fuentes de datos

  • Nombre : Los archivos deben llamarse <feature>.service.ts

  • Patrón : Interacciones de API puras con lógica mínima

Capa de utilidades ( src/utils/*.util.ts )

  • Propósito : Proporcionar funcionalidad compartida en toda la aplicación.

  • Utilidades clave :

    • logger.util.ts : Registro estructurado

    • error.util.ts : Manejo de errores y estandarización

    • formatter.util.ts : ayudantes de formato Markdown

Guía de desarrollo

Scripts de desarrollo

# 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]

Pruebas

# Run all tests
npm test

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

# Generate test coverage report
npm run test:coverage

evaluaciones

El paquete evals carga un cliente mcp que ejecuta el archivo index.ts, por lo que no es necesario reconstruir entre pruebas. Puede cargar variables de entorno prefijando el comando npx. Puede encontrar la documentación completa aquí .

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

Calidad del código

# Lint code
npm run lint

# Format code with Prettier
npm run format

# Check types
npm run typecheck

Creación de herramientas personalizadas

Siga estos pasos para agregar sus propias herramientas al servidor:

1. Definir la capa de servicio

Cree un nuevo servicio en src/services/ para interactuar con su API externa:

// 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. Crear controlador

Agregue un controlador en src/controllers/ para manejar la lógica empresarial:

// 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. Implementar la herramienta MCP

Crea una definición de herramienta en 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. Agregar compatibilidad con CLI

Cree un comando CLI en 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. Registrar componentes

Actualice los puntos de entrada para registrar sus nuevos componentes:

// 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);

Herramientas de depuración

Inspector de MCP

Acceda al Inspector visual de MCP para probar sus herramientas y ver los detalles de la solicitud/respuesta:

  1. Ejecutar npm run dev:server

  2. Abra http://localhost:5173 en su navegador

  3. Pruebe sus herramientas y vea los registros directamente en la interfaz de usuario

Registros del servidor

Habilitar registros de depuración para el desarrollo:

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

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

Publicación de su servidor MCP

Cuando esté listo para publicar su servidor MCP personalizado:

  1. Actualice package.json con sus datos

  2. Actualice README.md con la documentación de su herramienta

  3. Construya el proyecto: npm run build

  4. Pruebe la compilación de producción: npm run start:server

  5. Publicar en npm: npm publish

Licencia

Licencia ISC

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

Nota: Para garantizar la compatibilidad con versiones anteriores, el servidor también reconocerá las configuraciones con el nombre completo del paquete ( searchapi-mcp-server ) o con el nombre sin ámbito ( searchapi-mcp-server ) si no se encuentra la clave searchapi . Sin embargo, se recomienda usar la clave searchapi corta para las nuevas configuraciones.

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
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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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