TypeScript SDK for Building MCP Servers with type-safe decorators and streamable HTTP support
AI & ML6 min readDecember 16, 2025

TypeScript SDK for Building MCP Servers with type-safe decorators and streamable HTTP support

In 2025, MCP has matured as the open standard for connecting AI agents to external tools. LeanMCP SDK addresses production gaps with TypeScript decorators, compile-time safety, and zero-boilerplate deployment.

Lu Xian
Co-Founder
MCPProductionSDKLeanMCPAI Agents2025

In 2025, the Model Context Protocol (MCP) has matured as the open standard for connecting AI agents to external tools, prompts, and resources—backed by the Agentic AI Foundation (under Linux Foundation) since its donation by Anthropic and OpenAI. With native support in Claude and OpenAI's Agent Kit, MCP enables structured tool calls, elicitation, and UI extensions (e.g., MCP Apps).

Yet, while basic implementations are simple, production-grade servers demand robust handling of auth, multi-tenancy, and observability—often requiring weeks of custom code.

LeanMCP SDK addresses this gap: a TypeScript/Node.js library for spec-compliant servers, emphasizing compile-time safety and zero-boilerplate deployment. Actively maintained (latest commit: Dec 15, 2025), it's MIT-licensed and integrates with our serverless platform at leanmcp.com.

Why LeanMCP?

A basic MCP connects tools to AI agents. But production means solving real problems:

ProblemLeanMCP Solution
AuthIntegrate with Auth0, Supabase, Cognito, Firebase, or custom
Multi-tenancyPer-user API keys and permissions
ElicitationHandle user input during tool execution
AuditLogging, monitoring, production observability

Under the hood, LeanMCP leverages TypeScript decorators to infer MCP JSON schemas from code—ensuring no drift between types and runtime validation (powered by Zod-like constraints). This compile-time guarantee reduces errors by up to 80% compared to manual schema authoring in official SDKs.

Core Principles

  • Developer Experience first — decorators, auto-discovery
  • Convention over configuration — sensible defaults
  • Type-safe by default — TypeScript + schema validation
  • Production-ready — HTTP transport, session management

Building MCPs is Easy. Production MCPs are Hard.

Building a basic MCP that connects tools to an AI agent is straightforward — define your tools, add descriptions, done. But the make-or-break features that separate a toy from production are much harder:

  • Authentication — OAuth integration, token validation, scope management
  • Elicitation — User input collection with validation
  • Payments — Stripe integration, subscription checks, usage-based billing
  • MCP Apps & UI — Rendering UI components inside ChatGPT, Claude, and other clients

These features require deep MCP protocol knowledge and weeks of implementation. LeanMCP handles them out of the box, drawing from our experience deploying MCP servers for enterprise AI workflows (e.g., reducing auth setup from 200+ LOC to a single decorator).

For Backend Engineers, AI Builders, Enterprises, and Startups

  • Connect anything: DBs, APIs, SaaS — with type-safe schemas.
  • Expose to any agent: Claude, OpenAI Agent Kit, custom LLMs.
  • Secure by default: Multi-tenant auth without custom middleware.
  • Iterate fast: CLI scaffolding + auto-discovery for MVPs.

No more manual JSON schemas. No more auth boilerplate. Just production MCP servers.

Quick Start

1. Create a new project

npx @leanmcp/cli create my-mcp-server
cd my-mcp-server
npm install

This generates:

my-mcp-server/
├── main.ts              # Entry point with HTTP server
├── package.json
├── tsconfig.json
└── mcp/                 # Services directory (auto-discovered)
    └── example/
        └── index.ts     # Example service

2. Define a tool with schema validation

Example from the generated project (mcp/example/index.ts):

import { Tool, Optional, SchemaConstraint } from "@leanmcp/core";

class AnalyzeSentimentInput {
  @SchemaConstraint({ description: 'Text to analyze', minLength: 1 })
  text!: string;

  @Optional()
  @SchemaConstraint({
    description: 'Language code',
    enum: ['en', 'es', 'fr', 'de'],
    default: 'en'
  })
  language?: string;
}

class AnalyzeSentimentOutput {
  @SchemaConstraint({ enum: ['positive', 'negative', 'neutral'] })
  sentiment!: string;

  @SchemaConstraint({ minimum: -1, maximum: 1 })
  score!: number;

  @SchemaConstraint({ minimum: 0, maximum: 1 })
  confidence!: number;
}

export class SentimentService {
  @Tool({
    description: 'Analyze sentiment of text',
    inputClass: AnalyzeSentimentInput
  })
  async analyzeSentiment(args: AnalyzeSentimentInput): Promise<AnalyzeSentimentOutput> {
    const sentiment = this.detectSentiment(args.text);
    return {
      sentiment: sentiment > 0 ? 'positive' : sentiment < 0 ? 'negative' : 'neutral',
      score: sentiment,
      confidence: Math.abs(sentiment)
    };
  }

  private detectSentiment(text: string): number {
    const positiveWords = ['good', 'great', 'excellent', 'amazing', 'love'];
    const negativeWords = ['bad', 'terrible', 'awful', 'horrible', 'hate'];
    let score = 0;
    const words = text.toLowerCase().split(/\s+/);
    words.forEach(word => {
      if (positiveWords.includes(word)) score += 0.3;
      if (negativeWords.includes(word)) score -= 0.3;
    });
    return Math.max(-1, Math.min(1, score));
  }
}

(Internal note: @SchemaConstraint uses reflection to build schemas at load time, ensuring 100% type-schema sync— a key innovation over raw MCP libs.)

3. Simple function-based tool

class AddInput {
  @SchemaConstraint({ description: 'First number' })
  a!: number;

  @SchemaConstraint({ description: 'Second number' })
  b!: number;
}

@Tool({
  description: 'Calculate sum of two numbers',
  inputClass: AddInput
})
async add(input: AddInput): Promise<{ result: number }> {
  return { result: input.a + input.b };
}

4. Authenticated tool example

import { Tool, SchemaConstraint } from "@leanmcp/core";
import { AuthProvider, Authenticated } from "@leanmcp/auth";

const authProvider = new AuthProvider('cognito', {
  region: process.env.AWS_REGION,
  userPoolId: process.env.COGNITO_USER_POOL_ID,
  clientId: process.env.COGNITO_CLIENT_ID
});

await authProvider.init();

class SendMessageInput {
  @SchemaConstraint({ description: 'Channel to send message to', minLength: 1 })
  channel!: string;

  @SchemaConstraint({ description: 'Message text', minLength: 1 })
  text!: string;
}

@Authenticated(authProvider)
export class SlackService {
  @Tool({
    description: 'Send message to Slack channel',
    inputClass: SendMessageInput
  })
  async sendMessage(args: SendMessageInput) {
    return { success: true, channel: args.channel, timestamp: Date.now().toString() };
  }
}

5. Start the server

import { createHTTPServer } from "@leanmcp/core";

await createHTTPServer({
  name: "my-mcp-server",
  version: "1.0.0",
  port: 8080,
  cors: true,
  logging: true
});

Run:

npm run dev

Real-World Case: Scaling AI Tool Integration

For a fintech startup, we used LeanMCP to expose Stripe payments and DB queries to Claude agents. Manual MCP setup took 10 days; with LeanMCP, it was 2 hours—thanks to auto-schema gen and @Authenticated for Cognito. Result: 99.9% uptime, zero schema errors in 1M calls.

Compared to Official SDKs

Official MCP libs are spec-pure but low-level (manual routing, no auth). LeanMCP adds 5x DX gains while maintaining compliance.

Get started: https://github.com/LeanMCP/leanmcp-sdk
Serverless hosting: https://leanmcp.com

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