MCP Serverless: Automate & Scale AI Workflows

What is MCP Serverless: Automate & Scale AI Workflows?

MCP Serverless is a lightweight, serverless implementation of the Model Context Protocol (MCP) architecture designed to simplify AI workflow automation. It provides a clean interface for registering, managing, and executing tools—like machine learning models or API integrations—without requiring traditional server infrastructure. The core idea is to enable developers to focus on logic while the framework handles scaling and context-aware communication.

How to use MCP Serverless: Automate & Scale AI Workflows?

Getting started involves three key steps:

1. Install via npm: Run `npm install @tilfin/mcp-serverless` to add the package to your project.
2. Register tools: Define your tools with metadata (name, description, input schema) and business logic using the ToolManager class. For example, adding a calculator tool with authentication checks.
3. Create client-server connections: Use createService() to establish in-memory communication channels. You can call tools directly or inject context data (e.g., API keys) during execution.

Key Features of MCP Serverless: Automate & Scale AI Workflows?

• Serverless tool management: No need to manage servers—tools run on-demand within the framework's runtime.
• Context-aware execution: Pass credentials or session data via the ctx parameter to handle authentication or stateful workflows.
• Schema validation: Input schemas ensure tool arguments are validated before execution, reducing runtime errors.
• Transport flexibility: Built-in support for standard I/O (stdio) transports, with extensibility for custom communication layers.

Use cases of MCP Serverless: Automate & Scale AI Workflows?

Common scenarios include:

• Automated ML pipelines: Chain multiple models or data processors without manual orchestration.
• Serverless microservices: Deploy lightweight AI tools as serverless functions within larger applications.
• Secure API gateways: Use context parameters to enforce permissions or rate limits on sensitive tools.
• Real-time workflows: Handle low-latency tasks like sentiment analysis or image classification at scale.

FAQ from MCP Serverless: Automate & Scale AI Workflows?

• Does it require cloud infrastructure? No. The core is serverless but runs locally by default. Deploy to cloud via serverless platforms like AWS Lambda for wider scaling.
• How do I debug tool failures? Wrap toolFunction calls in try/catch blocks and log errors. Context parameters like apiKey are validated at runtime.
• Can I customize the transport layer? Yes. The stdio transport is a starting point—implement your own transport logic for HTTP, WebSocket, etc.
• What’s the performance overhead? Minimal. The in-memory architecture reduces latency, making it ideal for high-frequency, stateless tasks.

MCP Serverless

A serverless implementation for the Model Context Protocol (MCP) architecture that enables tool management through a clean interface.

Overview

This package provides a serverless implementation of the MCP server that allows you to:

• Register and manage tools
• Handle tool-related requests
• Create in-memory client-server connections
• Extend request with context to enable credential transmission from clients

Installation

npm install @tilfin/mcp-serverless

Usage

Tool Registration and create a client for service implements

import { createService, ToolManager } from '@tilfin/mcp-serverless';

// Create a tool manager
const toolManager = new ToolManager();

// Register tools
toolManager.registerTools([
  {
    name: 'calculator',
    description: 'Performs basic arithmetic operations',
    inputSchema: {
      type: 'object',
      properties: {
        operation: { type: 'string' },
        numbers: { type: 'array', items: { type: 'number' } }
      },
      required: ['operation', 'numbers']
    },
    toolFunction: async (params, ctx) => {
      if (ctx.apiKey !== 'xyz') throw new Error('Invalid API Key');

      let result;
      if (params.operation === 'add') {
        result = params.numbers.reduce((sum, n) => sum + n, 0);
      }
      return { result };
    }
  }
]);

// Create a serverless client
const client = createService(toolManager);

// List available tools
const toolsList = await client.listTools();

// Call a tool
try {
  const result = await client.callTool({
    name: 'calculator',
    arguments: {
      operation: 'add',
      numbers: [1, 2, 3]
    }
  });
} catch (err) {
  // raise Invalid API Key error
}

// Call a tool with context
const result = await client.callTool({
  name: 'calculator',
  arguments: {
    operation: 'add',
    numbers: [1, 2, 3]
  },
  ctx: { apiKey: 'xyz' }
});

API Reference

ToolManager class

Manages the registration and handling of tools.

Tool Interface

Tools must implement the following interface:

interface Tool {
  name: string;
  description: string;
  inputSchema: ToolInput;
  toolFunction: (args: CallToolRequestArguments, ctx: CallToolRequestContext) => Promise<CallToolResultContent>;
}

createServer(serverInfo, toolManager)

Creates an MCP server with the given tool manager.

createService(toolManager)

Creates an in-memory client-server setup for serverless operation.

Examples

Standard I/O Transport

The package includes examples for using the stdio transports for both client and server communication:

• StdioClientTransport: Allows clients to communicate with servers over standard input/output
• StdioServerTransport: Enables servers to handle requests through standard input/output

Check out the example implementation:

• Client: stdio_client.mjs
• Server: stdio_server.mjs