# Solving the M x N Integration Nightmare with Model Context Protocol

> Source: <https://pub.towardsai.net/solving-the-m-x-n-integration-nightmare-with-model-context-protocol-9754c6814741?source=rss----98111c9905da---4>
> Published: 2026-06-15 16:01:01+00:00

Here’s asimple, meaningful explanationof each section aboutModel Context Protocol (MCP)— you can use this for notes, docs, or interviews.

AI systems often fail not because models are weak, but because **they don’t have consistent access to the right context**. Data lives across many tools — databases, internal apps, chat systems, code repos — and integrating all of them manually is messy.

MCP exists to **standardize how AI connects to tools and data**, so models can work reliably across environments without custom glue code every time.

The Problem of Fragmentation:

If you have 5 AI apps and 5 databases, you have to write 25 different integrations, 5 x 5. If you add one more database, you have to update all 5 apps. It quickly becomes a nightmare.

This creates an **M × N integration problem**: M = number of AI apps, N = number of data sources

Each AI app needs custom integrations for each source.

Consequences:

👨💻 Common Problems as a Software Engineer:

Engineers working with AI tools run into:

🔧 Function Calling — What’s the Issue?

Function calling lets an LLM call APIs, but:

👉 Good for simple tasks, not for real systems.

🚀 Rise of Tools — New Complexity

We now have many:

But there’s **no universal standard** — leading to fragmentation again.

Every ecosystem reinvents integration.

⚠️ Imperfections of Tools & Problems with Tools

Most tools today suffer from:

Result: fragile AI systems.

🧠 Overview of Problem and Solution

AI lacks a universal way to:

Solution:

👉 MCP provides a **standard protocol layer** between AI and tools.

Think of MCP as:*“USB for AI integrations”*

Plug once — use anywhere.

📡 MCP Overview

MCP is a protocol that enables:

It defines how:

⚖️ MCP vs Tool Calling

Feature Tool Calling MCP State Stateless Stateful Workflow Linear Multi-step Vendor lock-in High Low Context sharing Limited Rich Orchestration Manual Built-in Portability Low High.

👉 Tool calling = single action

👉 MCP = coordinated system

🔌 MCP vs API

APIs like REST/ GraphQL are designed for developers, not AI.

**APIs lack:** Traditional APIs (like REST) were built for human developers typing out strict commands.

They don’t understand conversational context. MCP acts like an AI-native Translator on top of those APIs, allowing the AI to ask questions, discover what a tool can do naturally, and remember the conversation from the previous step.

MCP adds an AI-native layer on top, which can do conversational interaction with Host, automatically detect, understand, and integrate external tools, APIs, or operational competencies on the fly, without needing hardcoded programming.

🌟Benefits of MCP

🌍 MCP Ecosystem

The ecosystem typically includes:

As adoption grows, MCP could become the **default infrastructure layer for AI applications**.

🧩 One-Line Summary

👉 MCP standardizes how AI systems connect to tools and data, making integrations reliable, portable, and context-aware.

Host:

You can think of this analogy where the Host is like the restaurant dining room, or customer in the restaurant. Where main action happens (e.g. Claude Desktop)

The host is the AI application (e.g., Claude Desktop, VS Code, or an IDE) that runs the LLM and initiates connections to external services. It orchestrates workflows, maintains full conversation history, and enforces security by mediating all interactions.

Client:

The client is like the waiter in the restaurant who is serving the Host. Translates and carries the messages back and forth.

The client is a lightweight component created by the host — one per server connection — that manages bidirectional JSON-RPC communication. It handles protocol negotiation, capability exchange, subscriptions, notifications, and message routing while isolating servers from each other.

Server:

The server is like the kitchen in the restaurant. The isolated area that actually does the specific work (fetches data, runs a query) and hands it back.

The server is an independent process exposing specialized capabilities like tools, resources (data), prompts, or notifications from external systems (e.g., GitHub, Slack, databases). Servers operate in isolation, declare their features dynamically, and cannot access full context or other servers directly.

Benefits of MCP Architecture:

Primitives:

Primitives are things the **server** offers to the **host**

MCP Data Layer:

**Data Layer: **The data layer is the language and grammar of the MCP ecosystem that everyone agrees upon to communicate.

In MCP, JSON-RPC 2.0 serves as the foundation of the data layer.

Think of JSON-RPC as a standardized script. Instead of guessing how to ask for data, the AI sends a strict note: `Run this specific function with these specific details.`

JSON RPC 2.0 stands for JavaScript Object Notation Remote Procedure Call.

A **Remote Procedure Call (RPC) **allows a program to execute a function as if it were local, hiding the details of network communication and data transfer. This abstraction makes it easier to build distributed applications.

Instead of writing add(2,3) locally, you send a request to the server saying “please run add with parameters 2 and 3.”

JSON RPC combines the concept of Remote Procedure Calls with the simplicity of JSON, allowing developers to structure RPC requests and responses in standardized JSON format.

Why do we use JSON RPC in MCP?

Transport Layer

Now that our client and server speak the same language (JSON-RPC), how do they actually send messages to each other? It depends on where they live(Server Type):

```
stdin ( Input the program reads)stdout (Output the program writes)
```

2. **If they live on different computers (Remote):** They use **HTTP + SSE** (Server-Sent Events). This is just standard web traffic, allowing an AI app on your desktop to talk to a secure server running in the cloud.

The MCP life cycle is the complete sequence of steps that govern how a host (Client) and a server establish, use and end a connection during a session.

Stages of MCP Lifecycle:

Think of the **MCP Lifecycle** as a 3-step handshake:

Initialization:

The client sends an initialisation request to the server. The server sends its own capabilities and info. After successful initialization, the client MUST send an initialized notification to indicate it is ready to begin normal operations. Now the client and Server are connected.

```
{  "jsonrpc": "2.0",  "id": 1,  "method": "initialize",  "params": {    "protocolVersion": "2025-03-26", # MCP Protocol Version    "capabilities": {                # Client capabilities       "roots": {        "listChanged": true      },      "sampling": {}    },    "clientInfo": {                  # Client Implementation Info      "name": "ExampleMCPClient",      "version": "1.0.0"    }  }}
# # Step-2: Server-to-Client: initialize Response{  "jsonrpc": "2.0",  "id": 1,  "result": {    "protocolVersion": "2025-03-26", # MCP Protocol Version    "capabilities": {                # Server capabilities      "prompts": {        "listChanged": true      },      "resources": {        "subscribe": true,        "listChanged": true      },      "tools": {        "listChanged": true      }    },    "serverInfo": {                  # Server Implementation Info      "name": "ExampleMCPServer",      "version": "1.0.0"    }  }}# Step-3: Client-to-Server: initialized Notification{  "jsonrpc": "2.0",  "method": "notifications/initialized"}# Now Client and Server are Connected.1. The client MUST wait for the server's response to the initialize request before sending operational requests, except for pings. 2. The server MUST wait for the client's initialized notification before sending operational requests or custom responses, except for pings and logging messages.
```

**i. Client Proposal:** The client sends an initialize request containing the highest MCP version it supports (or a list of versions).

**ii. Server Decision:** The server reviews the version.

```
# Client Request (initialize):{  "jsonrpc": "2.0",  "id": 1,  "method": "initialize",  "params": {    "protocolVersion": "2024-11-05",    "capabilities": {},    "clientInfo": {      "name": "ExampleClient",      "version": "1.0.0"    }  }}# Server Response (Confirming Version):{  "jsonrpc": "2.0",  "id": 1,  "result": {    "protocolVersion": "2024-11-05",    "capabilities": {},    "serverInfo": {      "name": "ExampleServer",      "version": "2.4.1"    }  }}
# Client Request (Declaring Client Capabilities):{  "jsonrpc": "2.0",  "id": 2,  "method": "initialize",  "params": {    "protocolVersion": "2024-11-05",    "clientInfo": {      "name": "ClaudeDesktop",      "version": "1.2.0"    },    "capabilities": {      "experimental": {},      "sampling": {},      "roots": {        "listChanged": true      }    }  }}# Server Response (Declaring Server Capabilities):{  "jsonrpc": "2.0",  "id": 2,  "result": {    "protocolVersion": "2024-11-05",    "serverInfo": {      "name": "Postgres-Database-MCP",      "version": "1.0.0"    },    "capabilities": {      "resources": {        "subscribe": true,        "listChanged": true      },      "tools": {        "listChanged": true      },      "prompts": {}    }  }}
```

Operation:

During the operation phase, the client and server exchange messages according to the negotiated capabilities. Respect the negotiated protocol version. Only use capabilities that were successfully negotiated.

```
# Discover all tools present in the Server # Client Request:{  "jsonrpc": "2.0",  "id": 3,  "method": "tools/list"}# Server Response:{  "jsonrpc": "2.0",  "id": 3,  "result": {    "tools": [      {        "name": "execute_query",        "description": "Run a read-only SQL query against the database",        "inputSchema": {          "type": "object",          "properties": {            "sql": { "type": "string" }          },          "required": ["sql"]        }      }    ]  }}
# Client Request (tools/call):{  "jsonrpc": "2.0",  "id": 6,  "method": "tools/call",  "params": {    "name": "execute_query",    "arguments": {      "sql": "SELECT name, email FROM users WHERE status = 'active' LIMIT 3;"    }  }}# Server Response:{  "jsonrpc": "2.0",  "id": 6,  "result": {    "content": [      {        "type": "text",        "text": "[{\"name\": \"Alice\", \"email\": \"alice@example.com\"}, {\"name\": \"Bob\", \"email\": \"bob@example.com\"}]"      }    ],    "isError": false  }}
```

Shutdown of MCP:

One side (typically the client) initiates shutdown. No special JSON RPC shutdown messages are defined. The transport layer is responsible for signalling.

i. **Shutdown in STDIO**:

**a) Client-initiated shutdown:**

**b) Server-Initiated Shutdown: **When a local STDIO server needs to shut down independently (for example, due to a critical internal error, an unhandled exception, or a self-assigned idle timeout), it initiates the teardown by **closing its standard output (****stdout) or standard error (****stderr) streams**.

**ii**. **Shutdown in HTTP:**

**a) Client-initiated shutdown: **When the host application no longer needs the server’s capabilities, it takes the initiative to clean up the network footprint:

b)** Server-Initiated Shutdown (The Resilient Case)**: In a remote architecture, a server might need to go offline for maintenance, experience a deployment update, or encounter an internal error:

Special Cases in MCP:

``` php
# Client --> Server Ping Request{  "jsonrpc": "2.0",  "id": 100,  "method": "ping"}# Server --> Client Ping Response{  "jsonrpc": "2.0",  "id": 100,  "result": {}}
```

If no response is received within a reasonable timeout period, the sender MAY: a) consider the connection stale, b) terminate the connection, c) Attempt reconnection procedures

How use MCP

**Type of Connections**

**What is a Connector?**

A connector is a built-in feature that automatically links Claude to MCP, eliminating the need for manual setup or configuration.

This keeps things easy, safe and Consistent.

**Why not always use Connectors?**

How to make a server and client?

Quick Start Of MCP using FASTMCP

Step-1:

```
pip install fastmcp# check fastmcp versionfastmcp version
```

Step-2:

```
i) Create a project folder name "MY MCP"ii) Open the folder in VS Code iii) create to file 'my_server.py' and 'my_client.py'
```

Step-3: my_server.py

``` python
from fastmcp import FastMCPmcp=FastMCP("First MCP Server")## Add tool to the MCP server@mcp.tooldef greet(name: str) -> str:    """Greet a person by name."""    return f"Hello, {name}!"@mcp.tooldef add(a: float, b: float) -> float:    """Add two numbers."""    return a + bif __name__ == "__main__":    mcp.run(transport="http", port=8000)
```

Step-4: my_client.py

``` python
import asynciofrom fastmcp import Clientclient=Client("http://localhost:8000/mcp")async def call_tool(name: str):    """Call the greet tool on the MCP server."""    async with client:        result = await client.call_tool("greet", {"name": name})        print(result)        asyncio.run(call_tool("Debasish"))
```

Step-5:

```
# run server fastmcp run my_server.py:mcp --transport http --port 8000
# run client python my_client.py
```

The Model Context Protocol isn’t just another framework; it is the **universal adapter** the AI ecosystem desperately needed. By separating the client application from the underlying tools and data sources, MCP effectively destroys the M x N integration nightmare.

Whether you are building complex multi-agent workflows for production or just trying to give an LLM access to your local notes, using MCP ensures your integrations remain modular, highly secure, and entirely model-agnostic.

What are you planning to build with MCP? Let me know in the comments below! If you found this guide helpful, don’t forget to leave some claps 👏 and follow for more practical AI engineering guides.

[Solving the M x N Integration Nightmare with Model Context Protocol](https://pub.towardsai.net/solving-the-m-x-n-integration-nightmare-with-model-context-protocol-9754c6814741) was originally published in [Towards AI](https://pub.towardsai.net) on Medium, where people are continuing the conversation by highlighting and responding to this story.