MCP TypeScript SDK - Because Every AI Integration Shouldn't Be a Snowflake

MCP (Model Context Protocol) is Anthropic's attempt to standardize how AI apps access external data. Released in November 2024, it's gaining traction because it solves a real problem: every AI integration requires custom code.

The Reality Check

MCP is early-stage tech with a small but growing ecosystem. Companies like Block, Apollo, Replit, and Zed are experimenting with it, but most implementations are still prototypes, not production systems.

When to Use It:

• You want Claude Desktop to access your local files
• Building internal tools that AI assistants need to query
• Prototyping AI integrations without custom API work
• You're already comfortable with TypeScript/Node.js

When to Skip It:

• You need something battle-tested for production
• Your team isn't familiar with TypeScript
• You're building simple one-off integrations
• You need to integrate with non-MCP AI systems

How It Actually Works

MCP servers expose three things to AI apps:

1. Resources - Files, database records, API responses (read-only data)
2. Tools - Functions the AI can call (database writes, API calls, file operations)
3. Prompts - Reusable templates for common AI tasks

Instead of HTTP requests, MCP uses stdin/stdout for local communication (faster, no network overhead) or HTTP for remote deployments. It's basically GraphQL but for AI context.

They built this because every AI integration was becoming a custom snowflake. Instead of writing OAuth flows for Gmail, Slack, GitHub, etc., you write one MCP server and it works with any MCP client. In theory.

TypeScript SDK: The Good and Bad

The Good:

• Full type safety if you're already using TypeScript
• Handles all the protocol complexity for you
• Works with Node.js 18+
• Has working examples you can actually run

The Bad:

• Documentation assumes you know how MCP works (you definitely don't on first try)
• Error messages are garbage - "Invalid request" tells you nothing about which field is wrong
• Still version 1.x - breaking changes every update
• Limited real-world production examples beyond GitHub's implementation

Anyway, enough bitching about the docs. Here's what actually works in practice...

Most people are still just fucking around with this - I wouldn't bet production on it yet. GitHub's MCP server is pretty much the only example that actually matters for real work. Everyone else is building internal tools and hoping they work.

The SDK is fast enough unless you're doing heavy database queries. Then you'll wait 2-3 seconds per operation and wonder why you didn't just build a REST API.

The real nightmare isn't performance - it's figuring out what data to expose without breaking security or overwhelming the AI with garbage. We gave Claude access to our entire PostgreSQL database once. It spent forever trying to understand our schema and then asked if we really needed 47 different user-related tables. Learned to expose specific views, not raw tables the hard way.

Actual Production Usage

The biggest production example is GitHub's official MCP server, which lets AI apps interact with Git

Hub repositories.

It handles authentication, repository access, and code operations.

A few companies are using MCP internally:

• Block and Apollo are experimenting with it for internal tools

• Replit, Codeium, and Sourcegraph are integrating MCP into their development platforms

• Claude Desktop supports MCP servers for local file access

But let's be real

• most current usage is experimental, not the kind of stuff you'd bet your job on.

What You Can Actually Build

File System Access:

Connect AI to local directories, git repos, or file servers. The stdio transport makes this surprisingly fast.

Database Queries: Expose read-only database access or specific operations.

Good for letting AI analyze your data without full database access.

API Wrappers: Turn REST APIs into MCP servers.

Sometimes easier than teaching the AI about complex API authentication.

Internal Tools: Connect AI to your CRM, ticketing system, or monitoring tools.

Most useful for companies that already have Claude for Work.

Setup

A basic MCP server takes 30 minutes if everything goes perfectly.

In reality, plan for 2 hours because npm will inevitably shit the bed and you'll get "ENOTFOUND" errors that have fuck-all to do with your actual code. Here's the official example that actually works:

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

const server = new McpServer({
  name: \"my-server\",
  version: \"1.0.0\"
});

// Add your resources and tools here (good luck figuring out what to expose)

const transport = new Stdio

ServerTransport();
await server.connect(transport);

The hardest part isn't the MCP SDK

• it's figuring out what data your AI actually needs access to without creating a security nightmare.

The stdio transport fails silently when your server crashes. Your tools just disappear from Claude and you spend 45 minutes wondering if it's a caching issue before realizing Node died with exit code 1. Add console.log statements everywhere or you'll lose your fucking mind debugging this thing.

Look, if you're new to this, start with the quickstart guide and TypeScript handbook, but expect the first setup to take way longer because the docs assume you already know MCP.

Deployment Options

Local Development: stdio transport works great for Claude Desktop integration.

No network config needed.

Remote Deployment: HTTP transport works but requires session management.

The documentation has examples for Express.js setup.

Serverless:

Technically possible with AWS Lambda or similar, but you lose the persistent connection benefits.

Docker: Works fine.

Most production deployments will probably go this route. See containerizing TypeScript Node.js apps for setup guides.

Performance and Limitations

The TypeScript SDK is fast enough for most use cases.

The stdio transport is particularly snappy for local operations. Notification debouncing helps with bulk operations.

Main limitations:

• Small ecosystem means fewer examples to learn from that aren't the same "hello world" todo app

• Error messages like "Transport error" tell you nothing

• could be network, could be your code, could be cosmic rays

• Breaking changes guaranteed

• every minor version update changes something important with minimal migration docs

• Limited tooling for debugging (though MCP Inspector helps when it works)

• Need to understand MCP concepts before you can build anything useful

• the docs don't hand-hold

The SDK handles protocol complexity well, but you still need to understand MCP concepts to build anything useful.