MCP vs Claude Skills: Infrastructure vs Instructions

AI agents need two things: access to data and instructions on what to do with it.

Anthropic shipped two solutions: MCP (Model Context Protocol) in November 2024 and Claude Skills in October 2025. They sound similar. They're not.

MCP is infrastructure. Skills are instructions. One is production-grade with security and reliability baked in. The other is essentially RAG with Markdown files. Let's break it down.

MCP: Production Infrastructure That Doesn't Break

MCP is an open protocol that connects AI agents to external systems. Not through fragile API wrappers or one-off integrations, but through standardized, persistent, secure connections.

Security First

This is where MCP shines and where most "AI integration" solutions fall apart.

Token Validation: Every MCP connection validates tokens at the protocol level. You're not rolling your own OAuth flows or managing session tokens in application code. The protocol handles it.

Secure Transports: MCP supports HTTPS and stdio with built-in authentication. Your data doesn't travel in plain text. Your credentials don't leak through logs.

Session Management: Persistent sessions mean you authenticate once, not on every request. But here's the key: sessions are scoped and can be revoked. You get both convenience and control.

Access Control: MCP servers define granular permissions. Your agent can read from a database but not write. It can search GitHub repos but not push code. This isn't an afterthought—it's in the protocol spec.

Reliability That Scales

Persistent Connections: Once connected, the agent maintains a session. No re-authentication hell. No connection drops forcing retries. The agent can work through multi-step workflows without losing context.

Standardized Error Handling: When something breaks (and it will), MCP gives you structured errors. Not vague HTTP 500s. Actual error codes you can handle programmatically.

JSON Schema Validation: Every tool, every response is schema-validated. Your agent doesn't have to guess the data structure. Type safety from the protocol up.

Production Deployments: Companies like Block use MCP in production. Remote deployments. Multiple concurrent agents. Real enterprise workloads. This isn't a demo toy.

The Ecosystem

Pre-built MCP servers exist for:

GitHub (code search, PR management)
PostgreSQL (database queries)
Slack (messaging, channel management)
Puppeteer (browser automation)
Google Drive (file access)

You don't build from scratch. You configure and deploy.

Real Use Case

An enterprise agent that:

Queries your internal database (via MCP)
Fetches relevant documents (via MCP)
Posts summaries to Slack (via MCP)
Maintains audit logs of all data access

All with token validation, session management, and error handling built-in. That's MCP.

Claude Skills: RAG with Markdown Files

Claude Skills launched with a lot of hype about "modular expertise" and "procedural knowledge." Strip away the marketing: it's progressive disclosure RAG using Markdown files.

How It Actually Works

The Index (Metadata): A YAML frontmatter (~100 tokens) that Claude scans at session start. This is your retrieval index. Claude checks if the Skill is relevant to the current task.

The Retrieval (Instructions): If relevant, Claude loads the full Markdown file (<5,000 tokens). This contains step-by-step instructions, examples, guidelines.

The Augmentation (Resources): Optional scripts or reference files loaded on-demand. Python scripts for data processing. Config files for formatting rules.

It's RAG. With files. That's it.

What It's Good For

Skills excel at repeatable workflows:

Brand guidelines (colors, fonts, tone)
Data analysis templates (always filter by X, format as Y)
Code style enforcement (use these patterns, avoid these)

Basically, anything you'd put in a company wiki or SOP document.

The Reality Check

Token-Efficient? Yes. Progressive disclosure means you don't load everything upfront.

Revolutionary? No. It's file-based retrieval with nice packaging.

Limitation: Skills can't access external data. They can tell Claude how to process data, but not where to get the data. That's MCP's job.

The Real Difference

Aspect	MCP	Claude Skills
Purpose	Infrastructure - connects to systems	Instructions - guides agent behavior
Security	Token validation, secure transports, access control	No security layer (just Markdown files)
Reliability	Persistent connections, error handling, production-ready	File-based, no connection management
Complexity	Requires server setup, configuration	Drop Markdown files in a folder
Token Cost	Can be high for complex schemas	Low, progressive disclosure
Data Access	Yes - live connections to databases, APIs, tools	No - only provides instructions
Open Standard	Yes - works with any LLM	Claude-specific (but concept is portable)

The Truth:

MCP handles security, reliability, and data access
Skills handle consistency and reusable instructions
MCP is infrastructure; Skills are documentation

When to Use What

Use MCP when:

You need live data from external systems
Security and access control matter
You're building for production
You need audit trails and error handling
Your agent integrates with enterprise tools

Use Claude Skills when:

You have repeatable workflows
You want consistent outputs (formatting, tone, structure)
You don't need external data
You want simple, file-based configuration

Use Both when:

Building serious agents
Skills guide how to use MCP-fetched data
Example: A Skill defines your data analysis format, MCP fetches the data from your database

The Winning Combination

Here's the pattern that actually works:

MCP fetches live data from your database
Claude Skills define how to analyze that data (filters, grouping, format)
Agent executes the workflow with both context (MCP) and instructions (Skills)

Without MCP, your agent has no data. Without Skills, your agent lacks consistency. Together, they're powerful.

But make no mistake: MCP is doing the heavy lifting. Skills are the guardrails.

Build with LeanMCP SDK

If you're building with MCP, you need proper tooling. The official SDK is verbose. LeanMCP SDK handles the boilerplate:

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

class DatabaseService {
  @Tool({ description: 'Query customer data' })
  async queryCustomers(input: { filter: string }) {
    // Your query logic
    // Security, validation, error handling - all built in
    return results;
  }
}

Security, session management, transport handling - all abstracted.

Resources

MCP is infrastructure. Skills are instructions. Know the difference. Build accordingly.