What is MCP?

MCP (Model Context Protocol) is a standardized protocol for exposing tools, resources, and prompts to AI applications/agents.

Agent / AI Application
        │
        │ MCP
        ▼
   MCP Server
        │
   ┌────┼────┐
   ▼    ▼    ▼
 REST  SDK  DB
 API

The MCP server doesn’t necessarily implement the business capability itself. It can wrap existing systems, APIs, SDKs, databases, etc.

The key question

Don’t ask: "Do I have many tools? Should I create MCP?"

Ask: "Do I need to expose these capabilities as a reusable, standardized AI interface?"

That’s the decision point.

When you SHOULD create an MCP Server

1. Multiple agents need the same capabilities

For example:

Finance Agent ─────┐
Procurement Agent ─┤
SRE Agent ─────────┤
Security Agent ────┤
                   ▼
             ServiceNow
              MCP Server

Tools:

get_incident()
create_incident()
update_incident()
search_incidents()
get_change_request()

Instead of implementing those integrations independently in every agent, create an MCP server.

2. Multiple AI applications/frameworks need the capability

For example:

OpenAI Agent
LangGraph Agent
Google ADK Agent
Claude Agent
Custom Enterprise Agent
        │
        ▼
    MCP Server
        │
        ▼
     SAP

MCP gives them a common interface.

3. You want capability discovery

An AI client can discover what the MCP server exposes.

Conceptually:

Client
   │
   │ "What can you do?"
   ▼
MCP Server
   │
   ├── search_customer
   ├── get_order
   ├── create_order
   └── cancel_order

This is much more useful in dynamic agent ecosystems.

4. You want a reusable AI capability boundary

Suppose your organization has:

SAP
ServiceNow
Salesforce
AWS
GitHub
Jira

Rather than every AI team building their own integration:

Agent A → SAP
Agent B → SAP
Agent C → SAP
Agent D → SAP

you can provide:

                 SAP MCP Server
                       │
          ┌────────────┼────────────┐
          ▼            ▼            ▼
       Agent A       Agent B      Agent C

That becomes an enterprise AI capability.

5. You want centralized governance

An MCP server can become a controlled boundary for:

• authentication
• authorization
• tool permissions
• audit
• logging
• rate limiting
• secrets
• policy enforcement
• tool versioning

For enterprise environments, this can be a significant reason.

When you SHOULD NOT create an MCP Server

1. Tool is only for one agent

Example:

Invoice Agent
     │
     ├── extract_invoice()
     ├── validate_invoice()
     ├── calculate_tax()
     └── generate_report()

If these capabilities are:

• private
• tightly coupled
• only needed by this agent
• unlikely to be reused

just use normal tools.

Agent
 │
 ├── Tool 1
 ├── Tool 2
 ├── Tool 3
 └── Tool 4

No MCP required.

2. Even 100 tools can still be normal tools

This is important.