MCP for Skeptics: Why a Protocol Matters More Than Another SDK

The Reasonable Skepticism

You've probably seen:

• LangChain (the OG)
• LlamaIndex
• AutoGPT
• BabyAGI
• Semantic Kernel
• ... and 50 others

Each promised to be "the way" to build AI applications. Many are fading. Why would MCP be different?

Protocol vs. Framework

What Frameworks Do

// Framework approach
import { AgentFramework } from 'cool-ai-framework'

const agent = new AgentFramework({
  model: 'gpt-4',
  tools: [...],
  memory: frameworkMemory
})

// You're locked in. Change frameworks = rewrite everything.

What Protocols Do

// Protocol approach
// MCP defines HOW things communicate, not WHAT you build

// Server A (any implementation)
server.exposeResource("customers", { ... })

// Client B (any implementation)
client.connectToServer("server-a")
const customers = await client.getResource("customers")

// Switch clients? Servers still work.
// Switch servers? Clients still work.

HTTP didn't lock you into one web framework. MCP doesn't lock you into one AI framework.

Why MCP Might Actually Stick

1. Anthropic's Backing

Not a startup that might pivot or disappear. Claude uses MCP natively. That's real adoption from day one.

2. Solves a Real Problem

// Before MCP: Each integration is custom
claude_integration.connect_to(salesforce)
claude_integration.connect_to(postgres)
claude_integration.connect_to(jira)
// 3 different implementations

// With MCP: One protocol
mcp_client.connect_to(salesforce_mcp_server)
mcp_client.connect_to(postgres_mcp_server)
mcp_client.connect_to(jira_mcp_server)
// Same interface, different servers

3. Actually Simple

MCP isn't trying to be everything:

• Resources: Data the model can access
• Tools: Actions the model can take
• Prompts: Templates for common operations

That's it. No "agent memory systems" or "cognitive architectures." Just clean interfaces.

The Honest Downsides

It's New

MCP was released in late 2024. The ecosystem is young. You'll hit rough edges.

Anthropic-First

While MCP is open, Anthropic built it. Other model providers haven't adopted it yet. That could change—or might not.

Another Thing to Learn

Yes, it's another concept. The bet is that learning one protocol beats learning N custom integrations.

When to Use MCP

Good Use Cases

• Building tools that should work with multiple AI systems
• Exposing company data to AI in a structured way
• Creating reusable context servers
• Already using Claude and want native integration

Wait and See

• Simple, one-off integrations (just call the API directly)
• Locked into a non-Anthropic ecosystem
• Prototyping where speed beats architecture

A Minimal Example

// An MCP server in ~30 lines
import { Server } from "@modelcontextprotocol/sdk/server";

const server = new Server({
  name: "company-context",
  version: "1.0.0"
});

// Expose a resource
server.setRequestHandler("resources/list", async () => ({
  resources: [{
    uri: "company://customers",
    name: "Customer Database",
    description: "All customer records with history"
  }]
}));

// Handle resource reads
server.setRequestHandler("resources/read", async (request) => {
  if (request.params.uri === "company://customers") {
    const customers = await db.getCustomers();
    return { contents: [{ text: JSON.stringify(customers) }] };
  }
});

server.listen();

That's a working MCP server. Claude (or any MCP client) can now query your customer database.

The Bet You're Making

If MCP becomes the standard way AI connects to data:

• Your MCP servers work with any compliant AI
• Your investment compounds as the ecosystem grows
• You're ahead of companies still building one-off integrations

If MCP fades:

• You learned a clean architecture pattern
• Your server logic is still usable (just needs different transport)
• Time investment was modest

Skepticism is healthy. But the asymmetric bet favors learning MCP. Low downside, high upside.