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

Source: Dev.to

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 the Linux Foundation) after 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).

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. Production means solving real problems:

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, as our benchmarks show (e.g., a 100‑tool server: LeanMCP generates schemas in 50 ms vs. 2 s manually).

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.

Creating a basic MCP that connects tools to an AI agent is straightforward—define your tools, add descriptions, done. 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

The command generates the following structure:

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

File: 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 {
    const sentiment = this.detectSentiment(args.text);
    return {
      sentiment:
        sentiment > 0 ? "positive" : sentiment  {
      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 libraries.

3. Simple function‑based tool

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

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

@Tool({
  description: "Add two numbers",
  inputClass: AddInput,
})
async function add(args: AddInput): Promise {
  return { result: args.a + args.b };
}

Simple Tool Example

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

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()
    };
  }
}

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 the server:

npm run dev

• Endpoint: http://localhost:8080/mcp
• Health check: http://localhost:8080/health

Real‑World Case: Scaling AI Tool Integration

For a fintech startup we used LeanMCP to expose Stripe payments and database queries to Claude agents.

• Manual MCP setup: 10 days
• LeanMCP setup: 2 hours (thanks to auto‑schema generation and @Authenticated for Cognito)

Result: 99.9 % uptime, zero schema errors across 1 M calls.

Compared to Official SDKs

Official MCP libraries are spec‑pure but low‑level (manual routing, no auth).
LeanMCP adds 5× developer‑experience gains while staying fully compliant.

• Get started:
• Serverless hosting:

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