Model Context Protocol Server Setup Guide

Introduction

‍

As AI systems evolve from conversational assistants into tool-driven and action-oriented platforms, a standardized way to connect Large Language Models (LLMs) with backend capabilities becomes essential. Directly coupling prompts with APIs leads to tight dependencies, security risks, and poor scalability.

‍Model Context Protocol (MCP) addresses these challenges by defining a clear contract between AI systems and external tools. This blog provides a combined architectural view of both MCP Server and MCP Client, focusing on their roles, responsibilities, and interaction patterns. Example implementations are intentionally excluded to keep the discussion conceptual and enterprise-focused.

‍

What is Model Context Protocol (MCP)?

‍

Model Context Protocol (MCP) is an open protocol that standardizes how AI models:

‍

• Discover available tools
• Understand tool input and output schemas
• Invoke tools in a structured and secure manner

‍

MCP introduces a clean separation between:

• Reasoning (LLMs)
• Coordination (MCP Client)
• Execution (MCP Server)‍

‍

This separation is critical for building maintainable and governable AI systems.

‍

High-Level MCP Architecture

‍

An MCP-based system typically consists of:

‍

1. User Interface (UI)
2. Large Language Model (LLM)
3. MCP Client
4. MCP Server
5. Backend Systems / Services

‍

The MCP Client and MCP Server together form the control plane between AI reasoning and system execution.

‍

Role of the MCP Server

‍

An MCP Server is a backend service that exposes system capabilities as MCP-compliant tools.

‍

Core Responsibilities

‍

• Register and publish available tools
• Define strict input and output schemas
• Validate incoming requests
• Execute backend logic safely
• Enforce authentication and authorization
• Return structured responses

‍

‍Key Characteristics

‍

• Stateless and deterministic
• No AI or prompt logic
• Focused on execution and governance

‍

The MCP Server acts as an AI-facing capability gateway.

‍

‍Role of the MCP Client

‍

An MCP Client is responsible for coordinating interactions between the LLM and one or more MCP Servers.

‍

Core Responsibilities

‍

• Discover tools exposed by MCP Servers
• Present tool metadata and schemas to the LLM
• Send structured tool invocation requests
• Handle responses and errors
• Enforce client-side policies and routing

‍

Key Characteristics

‍

• Orchestration-focused
• LLM-aware but model-agnostic
• Supports multiple MCP Servers

‍

The MCP Client ensures that tool usage remains controlled and predictable.

‍

MCP Server and Client Interaction Flow

‍

1. The MCP Client requests tool metadata from the MCP Server
2. Tool definitions are shared, including schemas
3. The LLM selects an appropriate tool based on user intent
4. The MCP Client invokes the selected tool
5. The MCP Server validates and executes the request
6. The response is returned to the MCP Client
7. The LLM uses the result to generate the final response

‍

This flow maintains a strict boundary between reasoning and execution.

‍

Tool Definition and Schema Design

‍

In MCP, a tool represents a single, well-defined capability.

‍

Each tool definition includes:

• Unique tool name
• Clear description for LLM reasoning
• Input schema with explicit types
• Output schema with predictable structure

‍

Strong schema design improves reliability and reduces ambiguous tool usage.

‍

Security Model in MCP

‍

Security responsibilities are shared across MCP components:

‍

MCP Server Security

‍

• Authentication (API keys, OAuth, tokens)
• Authorization at tool level
• Input validation and sanitization
• Execution isolation
• Audit logging

‍

MCP Client Security

‍

• Controlled access to MCP Servers
• Policy-based tool invocation
• Request throttling and retries
• Observability and tracing

‍

A defense-in-depth approach is recommended.

‍

Scalability and Deployment Considerations

‍

MCP Server

‍

• Stateless design enables horizontal scaling
• Tool versioning prevents breaking changes
• Asynchronous execution improves throughput

‍

MCP Client

‍

• Can aggregate multiple MCP Servers
• Supports routing and load balancing
• Acts as a single integration point for LLMs

‍

Together, they enable scalable multi-tool AI platforms.

‍

Best Practices

‍

• Keep MCP Servers focused on execution only
• Avoid embedding AI logic in MCP Servers
• Use MCP Clients for orchestration and policy enforcement
• Design tools with minimal and explicit scope
• Version tools and schemas carefully
• Log and monitor all tool interactions

‍

MCP vs Traditional API Integration

‍

‍

Conclusion

‍

Model Context Protocol introduces a structured and scalable way to connect AI systems with real-world capabilities.

By clearly defining the responsibilities of MCP Servers and MCP Clients, organizations can build AI platforms that are secure, maintainable, and enterprise-ready.

MCP should be treated as foundational infrastructure for any production-grade, tool-driven AI system.