SearchAPI.site - MCP Server

This project provides a Model Context Protocol (MCP) server that connects AI assistants to external data sources (Google, Bing, etc.) via SearchAPI.site.

SearchAPI.site

- Website
- API Docs
- Swagger UI Config
- Create Search API key here
- GitHub

Todo

- [x] Support "stdio" transport
- [ ] Support "sse" transport

How to use

$3

``bash


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
`

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

For remote configuration:
`json
{
"mcpServers": {
"searchapi": {
"type": "sse",
"url": "https://mcp.searchapi.site/sse"
}
}
}
`

---

Source Code Overview

What is MCP?

Model Context Protocol (MCP) is an open standard that allows AI systems to securely and contextually connect with external tools and data sources.

This boilerplate implements the MCP specification with a clean, layered architecture that can be extended to build custom MCP servers for any API or data source.

Why Use This Boilerplate?

- Production-Ready Architecture: Follows the same pattern used in published MCP servers, with clear separation between CLI, tools, controllers, and services.

- Type Safety: Built with TypeScript for improved developer experience, code quality, and maintainability.

- Working Example: Includes a fully implemented IP lookup tool demonstrating the complete pattern from CLI to API integration.

- Testing Framework: Comes with testing infrastructure for both unit and CLI integration tests, including coverage reporting.

- Development Tooling: Includes ESLint, Prettier, TypeScript, and other quality tools preconfigured for MCP server development.

---

Getting Started

Prerequisites

- Node.js (>=18.x): Download
- Git: For version control

---

Step 1: Clone and Install

`bash


Clone the repository

git clone https://github.com/mrgoonie/searchapi-mcp-server.git
cd searchapi-mcp-server

Install dependencies

npm install
`

---

Step 2: Run Development Server

Start the server in development mode:

`bash
npm run dev:server
`

This starts the MCP server with hot-reloading and enables the MCP Inspector at http://localhost:5173.

⚙️ Proxy server listening on port 6277
🔍 MCP Inspector is up and running at http://127.0.0.1:6274

---

Step 3: Test the Example Tool

Run the example IP lookup tool from the CLI:

`bash


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"
`

---

Architecture

This boilerplate follows a clean, layered architecture pattern that separates concerns and promotes maintainability.

Project Structure

`
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
`

Layers and Responsibilities

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- Purpose: Define command-line interfaces that parse arguments and call controllers
- Naming: Files should be named .cli.ts
- Testing: CLI integration tests in .cli.test.ts

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- Purpose: Define MCP tools with schemas and descriptions for AI assistants
- Naming: Files should be named .tool.ts with types in .types.ts
- Pattern: Each tool should use zod for argument validation

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- Purpose: Implement business logic, handle errors, and format responses
- Naming: Files should be named .controller.ts
- Pattern: Should return standardized ControllerResponse objects

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- Purpose: Interact with external APIs or data sources
- Naming: Files should be named .service.ts
- Pattern: Pure API interactions with minimal logic

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- Purpose: Provide shared functionality across the application
- Key Utils:
- logger.util.ts: Structured logging
- error.util.ts: Error handling and standardization
- formatter.util.ts: Markdown formatting helpers

---

Development Guide

Development Scripts

`bash


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]
`

Testing

`bash


Run all tests

npm test

Run specific tests

npm test -- src/path/to/test.ts

Generate test coverage report

npm run test:coverage
`

Code Quality

`bash


Lint code

npm run lint

Format code with Prettier

npm run format

Check types

npm run typecheck
`

---

Building Custom Tools

Follow these steps to add your own tools to the server:

1. Define Service Layer

Create a new service in src/services/ to interact with your external API:

`typescript
// 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 {
logger.debug('Getting data', { param });
// API interaction code here
return { result: 'example data' };
}
`

2. Create Controller

Add a controller in src/controllers/ to handle business logic:

`typescript
// 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 {
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. Implement MCP Tool

Create a tool definition in src/tools/:

`typescript
// 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;

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. Add CLI Support

Create a CLI command in src/cli/:

`typescript
// 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 ', '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. Register Components

Update the entry points to register your new components:

`typescript
// 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 Inspector

Access the visual MCP Inspector to test your tools and view request/response details:

1. Run npm run dev:server
2. Open http://localhost:5173 in your browser
3. Test your tools and view logs directly in the UI

Server Logs

Enable debug logs for development:

`bash


Set environment variable

DEBUG=true npm run dev:server

Or configure in ~/.mcp/configs.json

`

---

Publishing Your MCP Server

When ready to publish your custom MCP server:

1. Update package.json with your details
2. Update README.md with your tool documentation
3. Build the project: npm run build
4. Test the production build: npm run start:server
5. Publish to npm: npm publish

---

License

ISC License

`json
{
"searchapi-mcp-server": {
"environments": {
"DEBUG": "true",
"ANY_OTHER_CONFIG": "value"
}
}
}
`

Note: For backward compatibility, the server will also recognize configurations under the full package name (@aashari/boilerplate-mcp-server) or the unscoped package name (boilerplate-mcp-server) if the boilerplate key is not found. However, using the short boilerplate` key is recommended for new configurations.