MCP Server - JavaScript SDK: Empowering AI Applications Through Model Context Protocol

Overview: What is MCP Server - JavaScript SDK?

The MCP (Model Context Protocol) Server - JavaScript SDK is an essential component for integrating Model Context Protocol into AI applications. It enables developers to build robust, interoperable systems that can connect with various AI clients and tools through a standardized protocol. This server allows you to define the context in which your AI application operates—capturing prompts, resources, and tools that are crucial for enhancing its functionality.

🔧 Core Features & MCP Capabilities

The MCP Server - JavaScript SDK is built on core capabilities derived from Model Context Protocol (MCP). These features include:

Defining Prompts

Prompts define the context in which an AI application operates. With this SDK, you can create prompts that describe specific scenarios or tasks. For example, a simple prompt might be designed to say "Hello, world!" when executed.

// prompts.js
export const prompts = {
    hello_world: {
        description: 'A simple prompt that says hello.',
        arguments: [],
        messages: [{
            role: 'assistant',
            content: {
                type: 'text',
                text: 'Hello, world!'
            }
        }]
    }
};

Managing Resources

Resources are external data sources or APIs that can be accessed and utilized by your AI application. This SDK allows you to define these resources in a structured manner.

// resources.js
export const resources = {
    apiReference: {
        uri: 'https://api.example.com/openapi.json',
        mimeType: 'application/json'
    }
};

Implementing Tools

Tools are the actual functionality that your AI application can perform. They can range from simple functions to complex operations.

// tools.js
export const tools = {
    simple_tool: {
        description: 'A simple tool',
        handler: async () => new Date().toLocaleString(),
        schema: {
            type: 'object',
            properties: {},
            required: []
        }
    },
    complex_tool: {
        description: 'A complex tool',
        handler: async ({ param1, param2 }) => {
            return `param1: ${param1}, param2: ${param2}`;
        },
        schema: {
            type: 'object',
            properties: {
                param1: { type: 'string' },
                param2: { type: 'string' }
            },
            required: ['param1', 'param2']
        }
    }
};

⚙️ MCP Architecture & Protocol Implementation

The architecture of the MCP Server - JavaScript SDK is designed to be flexible and extensible. It uses a modular approach where you can define individual prompts, resources, and tools without affecting others. The server instance encapsulates these components into a cohesive system that adheres to the Model Context Protocol.

Protocol Flow Diagram

Below is an example of how data flows through the MCP protocol:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram illustrates the interaction between an AI application, MCP clients, and the server. The server acts as a middleware layer that translates requests from various MCP clients into appropriate responses.

Client Compatibility Matrix

The compatibility matrix details which tools are supported by different MCP clients:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

This table helps developers understand which features are available when integrating the MCP server with different clients.

🚀 Getting Started with Installation

To get started, follow these steps to install and configure the MCP Server - JavaScript SDK:

1. Create Prompts File

   Define prompts in a file named prompts.js:

   export const prompts = {
       hello_world: {
           description: 'A simple prompt that says hello.',
           arguments: [],
           messages: [{
               role: 'assistant',
               content: {
                   type: 'text',
                   text: 'Hello, world!'
               }
           }]
       }
   };
   

2. Create Resources File

   Define resources in a file named resources.js:

   export const resources = {
       apiReference: {
           uri: 'https://api.example.com/openapi.json',
           mimeType: 'application/json'
       }
   };
   

3. Create Tools File

   Implement tools in a file named tools.js:

   export const tools = {
       simple_tool: {
           description: 'A simple tool',
           handler: async () => new Date().toLocaleString(),
           schema: {
               type: 'object',
               properties: {},
               required: []
           }
       },
       complex_tool: {
           description: 'A complex tool',
           handler: async ({ param1, param2 }) => {
               return `param1: ${param1}, param2: ${param2}`;
           },
           schema: {
               type: 'object',
               properties: {
                   param1: { type: 'string' },
                   param2: { type: 'string' }
               },
               required: ['param1', 'param2']
           }
       }
   };
   

4. Create Server Instance

   Create the server instance in a file named server.js:

   import { MCP } from 'mcp-server';
   import { tools } from './tools.js';
   import { prompts } from './prompts.js';
   import { resources } from './resources.js';
   
   const infos = {
       name: 'mcp-demo-server',
       version: '0.1.0'
   };
   
   const server = new MCP(infos, prompts, resources, tools);
   

5. Run the Server

   Finally, start the server to see it in action:

   node server.js
   

💡 Key Use Cases in AI Workflows

Scenario 1: Event Calendar Integration

Imagine an AI application that needs to create events based on user inputs and calendar data. By integrating the MCP Server - JavaScript SDK, you can define prompts for user input, tools for interacting with a calendar API, and resources for accessing event data.

// Define the context for creating an event
export const prompts = {
    create_event: {
        description: 'Input details to create a new event',
        arguments: []
    }
};

// Tools for handling calendar interactions
export const tools = {
    addEvent: {
        description: 'Add an event',
        handler: async (data) => {
            // Code to insert an event in the Calendar API
            return "Event added successfully.";
        },
        schema: {
            type: 'object',
            properties: {
                date: { type: 'string' },
                summary: { type: 'string' }
            }
        }
    }
};

// Server setup
const server = new MCP(infos, prompts, resources, tools);

Scenario 2: Document Summarization

Create an AI application that summarizes long texts. By defining a prompt for input and creating a tool to handle summarization tasks using NLP libraries, you can integrate these components into the MCP Server.

// Define the context for text summarization
export const prompts = {
    summarize_text: {
        description: 'Input a lengthy text for summary',
        arguments: []
    }
};

// Tool for handling summarization tasks
export const tools = {
    summarize: {
        description: 'Summarize text input',
        handler: async (text) => {
            // Code to perform NLP analysis and generate summary
            return "Summary generated successfully.";
        },
        schema: {
            type: 'object',
            properties: {
                content: { type: 'string' }
            }
        }
    }
};

// Server setup
const server = new MCP(infos, prompts, resources, tools);

🔌 Integration with MCP Clients

The MCP Server - JavaScript SDK is compatible with various MCP clients such as Claude Desktop and Continue. This ensures that your AI application can seamlessly connect to a wide range of platforms.

Configuration Code Sample

Here's an example configuration snippet for integrating the server into an MCP client:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

📊 Performance & Compatibility Matrix

The performance and compatibility of the MCP Server - JavaScript SDK have been tested with different MCP clients. The following table outlines its current status:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

This matrix helps developers understand the specific features supported by each client.

🛠️ Advanced Configuration & Security

Customizing Server Behavior

The MCP server allows for extensive customization, enabling developers to tailor its behavior according to their requirements. For instance, you can define custom error handling and logging mechanisms.

Security Best Practices

Securing your MCP integration is crucial. Here are some best practices:

• Use secure communication protocols.
• Implement authentication and authorization.
• Regularly update dependencies.

❓ Frequently Asked Questions (FAQ)

1. Can I use this server with multiple clients simultaneously?

   • Yes, you can configure the server to support multiple MCP clients concurrently. Each client would have its own set of configurations defined.

2. What tools are currently available in the SDK?

   • The SDK includes basic tool definitions for tasks such as simple date functions and complex operations like NLP summarization.

3. Why is Cursor listed as 'Tools Only' in the compatibility matrix?

   • Cursor prefers to integrate only specific tools rather than full resources or prompts due to its unique architecture.

4. How do I handle errors in the server?

   • You can set up custom error handlers in your tool definitions to manage any issues that arise during execution.

5. Is there a limit to the number of prompts, resources, and tools I can define?

   • There is no strict limit; however, best practices recommend modular design for maintenance purposes.

👨‍💻 Development & Contribution Guidelines

To contribute to the MCP Server - JavaScript SDK:

1. Fork the Repository

   Fork the official repository on GitHub and clone it locally.

2. Contribute Code Changes

   Make your contributions by submitting pull requests with clear descriptions of changes made.

3. Run Unit Tests

   Ensure that all unit tests pass before submitting a merge request.

🌐 MCP Ecosystem & Resources

The MCP ecosystem includes various tools, libraries, and resources to help developers build and integrate AI applications effectively. Join the community for more updates and support:

• Visit Model Context Protocol's official website for detailed documentation.
• Follow the GitHub repository for the latest updates.

By leveraging the power of the MCP Server - JavaScript SDK, developers can build robust AI applications that are seamlessly interoperable with multiple clients, enhancing their functionality and reach.