🧠 First: What is MCP?

MCP (Model Context Protocol) is a standard that lets AI models securely interact with external tools, APIs, and systems.

Think of it as:

“USB-C for AI tools”
A universal way for AI agents to call tools like pricing engines, ERP systems, or decision services.

Instead of hard-coding integrations, you expose tools through an MCP server, and any MCP-compatible AI client can use them.

In the example we built:

A Make-vs-Buy decision engine is exposed as an MCP tool.

🏗️ Core MCP Concepts (Explained via Make-vs-Buy Example)

We’ll break MCP into its essential building blocks.

AI Agent (Client)
   ↓
MCP Client
   ↓
MCP Server  → Tools → Decision logic → Data

1. MCP Server

An MCP server is a program that exposes tools and resources to AI systems.

In our case:

Make vs Buy MCP Server

It exposes a tool:

make_vs_buy_decision

Responsibilities of MCP server

Define tools
Validate input schema
Run business logic
Return structured output
Provide context to AI safely

In example

The server calculates:

Manufacturing cost
Supplier cost
Margin
Decision

It behaves like a decision microservice for AI agents.

2. MCP Client

The MCP client sits inside:

AI agent
LLM app
automation system
chatbot
ERP copilot

It connects to MCP servers and calls tools.

Client responsibilities

Discover tools
Send inputs
Receive structured output
Give result back to AI

Example flow

AI Agent: Should we manufacture this product?
Client → calls make_vs_buy_decision tool
Server → calculates
Client → returns decision to AI
AI → explains to user

So MCP client = bridge between AI and tools.

3. MCP Tools (Most Important Concept)

Tools are functions exposed by MCP server.

Each tool has:

Name
Description
Input schema
Output

Example tool

make_vs_buy_decision

Input schema

selling_price
raw_material_cost
labor_cost
supplier_price
logistics_cost
risk_factor
expected_demand

Output

decision: MAKE/BUY
cost breakdown
margin
reasoning

Why tools matter

LLMs cannot calculate real business logic reliably.
Tools allow AI to:

Fetch real data
Run real formulas
Call real systems
Return deterministic output

4. Structured Inputs and Outputs

MCP uses strong structured schemas.

Why important?

AI doesn’t guess parameters
Ensures correctness
Enables automation
Enables chaining

Example input JSON

{
  selling_price: 120,
  expected_demand: 1000,
  raw_material_cost: 40,
  labor_cost: 20,
  supplier_unit_price: 95
}

Output JSON

{
 decision: "MAKE",
 make_cost_per_unit: 85,
 buy_cost_per_unit: 103,
 reasoning: "Manufacturing cheaper"
}

Structured output lets:

AI explain results
ERP consume results
dashboards use results

5. Context Separation (Critical MCP Concept)

LLMs should NOT directly access:

ERP
Cost databases
supplier systems
internal pricing

MCP provides a safe middle layer.

LLM → MCP server → business systems

Benefits:

Security
Auditability
Controlled access
Deterministic logic

In our example:
LLM cannot calculate cost directly.
It must call MCP tool.

6. Tool Discovery

MCP clients can ask server:

What tools do you provide?

Server responds:

make_vs_buy_decision
get_supplier_risk
forecast_demand

This allows dynamic AI systems.

Example:
A supply chain agent can automatically discover:

pricing tools
inventory tools
sourcing tools

7. Decision Engine as MCP Tool

Your Make-vs-Buy logic becomes reusable.

Instead of embedding logic in:

ERP
Excel
scripts

You expose it once as MCP tool.

Then:

AI agents use it
dashboards use it
procurement bots use it
planners use it

This is decision-as-a-service.

8. Deterministic vs Generative Responsibilities

Important MCP concept:

Component	Responsibility
LLM	reasoning, explanation
MCP Tool	calculation, decision
Database	truth
AI Agent	orchestration

In our example

LLM:

“Should we make or buy?”

MCP tool:

Calculates cost and returns decision

LLM:

Explains result to user

9. MCP Communication Model

Usually JSON-RPC style.

Client → tool call
Server → structured response

Example:

callTool("make_vs_buy_decision", {...})

Transport can be:

stdio (local)
HTTP
WebSocket
container
cloud

10. Composability (Advanced MCP Concept)

Multiple MCP tools can be chained.

Example supply chain agent:

1. forecast_demand tool
2. supplier_risk tool
3. make_vs_buy tool
4. pricing_optimizer tool

All exposed via MCP.

AI orchestrates them like Lego blocks.

11. Enterprise MCP Architecture

Without MCP

LLM → custom APIs → ERP → scripts → chaos

With MCP

LLM agent
   ↓
MCP client layer
   ↓
MCP servers:
  - Cost engine
  - Pricing engine
  - Supplier risk
  - Inventory
  - Negotiation AI

Everything standardized.

12. Real Production Version of This Example

Your simple make-vs-buy tool would expand to:

Inputs

plant capacity
MOQ
lead time
currency
forecast demand
supplier reliability
carbon cost

Logic

NPV analysis
scenario simulation
multi supplier comparison
optimization solver

Outputs

decision
risk score
ROI
sensitivity analysis
recommendation

13. Why MCP is Becoming Critical

MCP is to AI what REST APIs were to web.

It allows:

AI-native ERP
AI supply chain agents
autonomous procurement
decision automation

Without MCP:
AI = chat only
With MCP:
AI = decision maker

14. Mental Model to Remember

Think:

MCP server = brain extension for AI
Tools = capabilities
Client = connector
LLM = reasoning layer

Your make-vs-buy example:

AI gets a new “procurement brain” via MCP.

Industry 4.0 & AI

Tuesday, 10 February 2026

MCP (Model Context Protocol): Important concepts