MCP-Server-Starter

The Model Context Protocol (MCP) is a specialized framework designed to streamline the process of enabling AI agents to interact with a wide array of tools. This starter template helps you quickly build a Model Context Protocol (MCP) server using TypeScript. It provides a robust foundation that you can easily extend to create advanced MCP tools and seamlessly integrate them with various AI platforms.

• MCP Servers: These servers act as bridges, exposing APIs, databases, and code libraries to external AI hosts. By implementing an MCP server in TypeScript, developers can share data sources or computational logic in a standardized way using JSON-RPC 2.0.
• MCP Clients: These are the consumer-facing side of MCP, communicating with servers to query data or perform actions. MCP clients use TypeScript SDKs, ensuring type-safe interactions and uniform approach to tool usage.
• MCP Hosts: Systems such as Claude, Cursor, Windsurf, Cline, and other TypeScript-based platforms coordinate requests between servers and clients, ensuring seamless data flow. A single MCP server can thus be accessed by multiple AI hosts without custom integrations.

The MCP TypeScript SDK provides core classes for building servers:

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({
  name: "mcp-server-starter",
  version: "1.0.0",
  capabilities: {
    tools: {},      // Enable tools capability
    resources: {},  // Enable resource access
    prompts: {},    // Enable prompt handling
    streaming: true // Enable streaming responses
  }
});

// Connect transport
const transport = new StdioServerTransport();
await server.connect(transport);

By using MCP, developers no longer need complex custom code to integrate new tools or services. Instead, they build an MCP server and make it available to supported hosts.

• Node.js (v18 or later): A modern version of Node.js that takes advantage of the latest JavaScript features and performance improvements.
• npm (v7 or later): Ensures compatibility for installing and managing packages.
• VS Code with Dev Containers extension: Allows you to quickly spin up a reproducible development environment, making collaboration easier and more efficient.

A typical file layout for the MCP server template may look like this:

mcp-server/
├── .devcontainer/        # Dev container configuration
│   └── devcontainer.json
├── src/
│   ├── index.ts         # MCP Server main entry point
│   └── examples/        # Example tool implementations
│       ├── calculator.ts # Calculator tool example
│       └── rest-api.ts  # REST API tool example
├── package.json         # Project configuration
└── tsconfig.json        # TypeScript configuration

The .devcontainer directory streamlines container-based development, while the src/ folder houses the main server logic and examples of custom tools. This structure keeps your project organized and easy to navigate.

To install MCP Server Starter for any supported client:

# For Claude
npx -y @smithery/cli install @TheSethRose/mcp-server-starter --client claude

# For Cursor
npx -y @smithery/cli install @TheSethRose/mcp-server-starter --client cursor

# For Windsurf
npx -y @smithery/cli install @TheSethRose/mcp-server-starter --client windsurf

# For Cline
npx -y @smithery/cli install @TheSethRose/mcp-server-starter --client cline

# For TypeScript
npx -y @smithery/cli install @TheSethRose/mcp-server-starter --client typescript

1. Clone this template: Retrieve the repository files from your preferred source.
2. Open in VS Code with Dev Containers: If you have the Dev Containers extension installed, you will be prompted to open this project inside a container.
3. Install dependencies:

   npm install

   This command fetches and installs all required packages for the MCP server.
4. Build the project:

   npm run build

   This compiles your TypeScript code into JavaScript, preparing it for runtime.

• Build the project:

  npm run build

  Compiles your TypeScript source and sets file permissions for the main entry point.

• Watch mode:

  npm run watch

  Automatically recompiles TypeScript files whenever changes are made, ideal for active development.

• Run with inspector:

  npm run inspector

  Launches the server alongside a debugging tool, enabling you to trace issues, set breakpoints, and inspect variables in real time.

MCP tools must return responses in a specific format to ensure proper communication with AI hosts. Here's the structure:

interface ToolResponse {
  content: ContentItem[];
  isError?: boolean;
  metadata?: Record<string, unknown>;
}

interface ContentItem {
  type: string;
  text?: string;
  mimeType?: string;
  data?: unknown;
}

Supported content types include:

• text: Plain text content
• code: Code snippets with optional language specification
• image: Base64-encoded images with MIME type
• file: File content with MIME type
• error: Error messages (when isError is true)

Example response:

return {
  content: [
    {
      type: "text",
      text: "Operation completed successfully"
    },
    {
      type: "code",
      text: "console.log('Hello, World!')",
      mimeType: "application/javascript"
    }
  ]
};

When developing MCP tools, follow these security guidelines:

1. Input Validation:

   • Always validate input parameters using Zod schemas
   • Implement strict type checking
   • Sanitize user inputs before processing
   • Use the strict() option in schemas to prevent extra properties

2. Error Handling:

   • Never expose internal error details to clients
   • Implement proper error boundaries
   • Log errors securely
   • Return user-friendly error messages

3. Resource Management:

   • Implement proper cleanup procedures
   • Handle process termination signals
   • Close connections and free resources
   • Implement timeouts for long-running operations

4. API Security:

   • Use secure transport protocols
   • Implement rate limiting
   • Store sensitive data securely
   • Use environment variables for configuration

Example secure tool implementation:

const SecureSchema = z.object({
  input: z.string()
    .min(1)
    .max(1000)
    .transform(str => str.trim())
    .pipe(z.string().regex(/^[a-zA-Z0-9\s]+$/))
});

server.tool(
  "secure_tool",
  SecureSchema.shape,
  async (params) => {
    try {
      // Implement rate limiting
      await rateLimiter.checkLimit();

      // Process validated input
      const result = await processSecurely(params.input);

      return {
        content: [{
          type: "text",
          text: result
        }]
      };
    } catch (error) {
      // Log error internally
      logger.error(error);

      // Return safe error message
      return {
        content: [{
          type: "text",
          text: "An error occurred processing your request"
        }],
        isError: true
      };
    }
  }
);

MCP supports streaming responses for long-running operations:

server.tool(
  "stream_data",
  StreamSchema.shape,
  async function* (params) {
    for (const chunk of dataStream) {
      yield {
        content: [{
          type: "text",
          text: chunk
        }]
      };
    }
  }
);

You can define custom content types for specialized data:

interface CustomContent extends ContentItem {
  type: "custom";
  data: {
    format: string;
    value: unknown;
  };
}

Implement proper async handling:

server.tool(
  "async_operation",
  AsyncSchema.shape,
  async (params) => {
    const operation = await startAsyncOperation();

    while (!operation.isComplete()) {
      await operation.wait();
    }

    return {
      content: [{
        type: "text",
        text: await operation.getResult()
      }]
    };
  }
);

Use Jest for testing your tools:

describe('Calculator Tool', () => {
  let server: McpServer;

  beforeEach(() => {
    server = new McpServer({
      name: "test-server",
      version: "1.0.0"
    });
    registerCalculatorTool(server);
  });

  test('adds numbers correctly', async () => {
    const result = await server.executeTool('calculate', {
      a: 5,
      b: 3,
      operation: 'add'
    });

    expect(result.content[0].text).toBe('8');
  });
});

1. MCP Inspector:

   npm run inspector

   Provides real-time inspection of:

   • Tool registration
   • Request/response flow
   • Error handling
   • Performance metrics

2. Logging:

   function logMessage(level: 'info' | 'warn' | 'error', message: string) {
     console.error(`[${level.toUpperCase()}] ${message}`);
   }

3. Error Tracking:

   process.on('uncaughtException', (error: Error) => {
     logMessage('error', `Uncaught error: ${error.message}`);
     // Implement error reporting
   });

MCP supports multiple transport protocols:

import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const transport = new StdioServerTransport();
await server.connect(transport);

import { WebSocketServerTransport } from "@modelcontextprotocol/sdk/server/websocket.js";

const transport = new WebSocketServerTransport({
  port: 3000
});
await server.connect(transport);

import { Transport } from "@modelcontextprotocol/sdk/server/transport.js";

class CustomTransport implements Transport {
  // Implement transport methods
}

Configure server capabilities:

const server = new McpServer({
  name: "mcp-server",
  version: "1.0.0",
  capabilities: {
    tools: {}, // Enable tools capability
    streaming: true, // Enable streaming support
    customContent: ["myFormat"], // Define custom content types
    metadata: true // Enable metadata support
  }
});

This MCP server template supports multiple AI platforms out of the box:

1. Claude Desktop:

   • Provides a chat-based environment
   • Supports all MCP capabilities
   • Ideal for conversational AI interactions

2. Cursor:

   • AI-powered development environment
   • Full tool integration support
   • Perfect for coding assistance

3. Windsurf:

   • Modern AI development platform
   • Complete MCP protocol support
   • Streamlined workflow integration

4. Cline:

   • Command-line AI interface
   • Tool-focused interactions
   • Efficient terminal-based usage

5. TypeScript:

   • Native TypeScript support
   • Type-safe tool development
   • Seamless SDK integration

Each client can be configured using the appropriate Smithery CLI command:

npx -y @smithery/cli run @TheSethRose/mcp-server-starter --client [client-name]

Replace [client-name] with one of: claude, cursor, windsurf, cline, or typescript.

A convenient way to run this MCP server is through Smithery, a centralized platform for discovering and publishing MCP servers. Smithery simplifies deployment and ensures your server can be integrated into various AI workflows.

You can immediately execute this server via the Smithery CLI:

npx -y @smithery/cli@latest run mcp-server-template --config "{}"

Smithery automatically fetches, installs, and runs the server from its latest release, requiring minimal setup from you.

If you have developed new tools or made local modifications and wish to share them, consider publishing your customized server:

1. Create an account on Smithery.
2. Follow their deployment instructions to bundle and publish your MCP server.
3. Other users can then run your server through Smithery by referencing your unique package name.

Smithery offers:

• A centralized registry to discover and share MCP servers.
• Simplified deployment, removing repetitive setup.
• A community-driven approach where developers contribute diverse tools.
• Easy integration with popular AI hosts.

For additional guidance:

• Smithery Documentation
• Smithery GitHub

Cursor is another AI development environment that supports MCP. To incorporate your server into Cursor:

1. Build your server:

   npm run build

   Ensure an executable index.js is generated in the build directory.

2. In Cursor, go to Settings > Features > MCP: Add a new MCP server.

3. Register your server:

   • Select stdio as the transport type.
   • Provide a descriptive Name.
   • Set the command, for example: node /path/to/your/mcp-server/build/index.js.

4. Save your configuration.

Cursor then detects and lists your tools. During AI-assisted coding sessions or prompt-based interactions, it will call your MCP tools whenever relevant. You can also instruct the AI to use a specific tool by name.

Claude Desktop provides a chat-based environment where you can leverage MCP tools. To include your server:

1. Build your server:

   npm run build

   Confirm that no errors occur and that the main script is generated in build.

2. Modify claude_desktop_config.json:

   {
     "mcpServers": {
       "mcp-server": {
         "command": "node",
         "args": [
           "/path/to/your/mcp-server/build/index.js"
         ]
       }
     }
   }

   Provide the path to your compiled main file along with any additional arguments.

3. Restart Claude Desktop to load the new configuration.

When you interact with Claude Desktop, it can now invoke the MCP tools you have registered. If a user's request aligns with any of your tool's functionality, Claude will prompt to use that tool.

1. Use TypeScript for better type checking, clearer code organization, and easier maintenance over time.
2. Adopt consistent patterns for implementing tools:
   • Keep each tool in its own file
   • Use descriptive schemas with proper documentation
   • Implement comprehensive error handling
   • Return properly formatted content
3. Include thorough documentation:
   • Add JSDoc comments to explain functionality
   • Document parameters and return types
   • Include examples where helpful
4. Leverage the inspector for debugging:

   npm run inspector

   This helps you:
   • Test tool functionality
   • Debug request/response flow
   • Verify schema validation
   • Check error handling
5. Test comprehensively before deployment:
   • Verify input validation
   • Test error scenarios
   • Check response formatting
   • Ensure proper integration with hosts
6. Follow MCP best practices:
   • Use proper content types
   • Implement proper error handling
   • Validate all inputs and outputs
   • Handle network requests safely
   • Format responses consistently

For further information on the MCP ecosystem, refer to:

• Model Context Protocol Documentation: Detailed coverage of MCP architecture, design principles, and more advanced usage examples.
• Smithery - MCP Server Registry: Guidelines for publishing your tools to Smithery and best practices for their registry.
• MCP TypeScript SDK Documentation: Comprehensive documentation of the TypeScript SDK.
• MCP Security Guidelines: Detailed security best practices and recommendations.

By following this template and best practices, you can quickly build a robust MCP server that opens your tools to a broad range of AI hosts. This expanded approach ensures easier maintenance, better type safety, and a smooth user experience when harnessing the capabilities of modern AI systems.

Template created by Seth Rose:

• Website: https://www.sethrose.dev
• 𝕏 (Twitter): https://x.com/TheSethRose
• 🦋 (Bluesky): https://bsky.app/profile/sethrose.dev

1. Type Safety:

   • Leverage TypeScript's type system for robust tool definitions
   • Use Zod schemas for runtime validation
   • Define clear interfaces for tool parameters and responses

2. Transport Selection:

   • Use StdioServerTransport for local process communication
   • Implement WebSocketServerTransport for network-based tools
   • Consider custom transports for specific use cases

3. Capability Management:

   • Clearly define server capabilities during initialization
   • Implement proper capability negotiation
   • Handle capability-specific errors gracefully

4. Security Considerations:

   • Implement user consent flows for sensitive operations
   • Validate all inputs using TypeScript types and Zod schemas
   • Handle errors securely without exposing internal details