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Understanding Model Context Protocol (MCP) Server - A Comprehensive Guide

Understanding Model Context Protocol (MCP) Server - A Comprehensive Guide
Deepak Kamboj
Senior Software Engineer
5 min read
AI

Modern AI workflows require more than just a prompt and a model — they demand context. In high-scale ML systems, especially those involving autonomous agents or dynamic LLM-based services, managing state, session, and data conditioning is essential. That’s where the Model Context Protocol (MCP) Server comes in.

In this blog post, we’ll walk through:

  • What an MCP Server is and why it’s needed
  • How it fits into AI/ML pipelines
  • Its component architecture
  • Real-world use cases
  • A walkthrough with TypeScript code snippets
  • Deployment and scaling considerations

🚀 What is the MCP Server?

The Model Context Protocol (MCP) Server is a middleware system designed to manage context and state between various components in an AI pipeline—particularly large language model (LLM) based agents.

It acts as:

  • A context-aware memory orchestrator
  • A router between NLP/ML agents and input sources
  • A validation and enrichment layer for incoming prompts

TIP: Think of MCP as the brain behind GenAI agents—storing past state, user history, and even shared goals, allowing multi-turn reasoning.


🧩 Why Do We Need MCP Servers?

Traditional prompt engineering is stateless, making it hard to support:

  • Multi-step workflows
  • Shared context across API boundaries
  • Prompt generation based on dynamic inputs (auth tokens, environment configs, etc.)

Without MCP, you end up writing glue code in every microservice. With MCP, prompt creation becomes declarative and context-driven.

IMPORTANT: In distributed LLM-based applications, losing context between calls can make outputs brittle, unpredictable, or irrelevant.


🏗️ MCP Server Architecture

Let’s break down how an MCP Server fits in your AI pipeline.

🔧 Core Components

  • Context Extractor: Extracts relevant information from inputs (e.g., user role, history).
  • Prompt Generator: Templates + context → dynamically generated prompt.
  • LLM Router: Routes to appropriate model based on use-case.
  • Response Handler: Parses model response, updates context, triggers downstream actions.

TIP: Using Redis or a vector DB (like Pinecone) as a context store allows retrieval-augmented generation (RAG) seamlessly.


🛠️ Code Walkthrough (TypeScript)

Let’s define the schema and a sample prompt generation service.

🔸 Types

export interface MCPRequest {
userId: string;
intent: string;
metadata?: Record<string, any>;
previousContextId?: string;
}

export interface MCPResponse {
contextId: string;
prompt: string;
model: string;
response: string;
}

🔸 Sample Prompt Generator

export function generatePrompt(intent: string, metadata: Record<string, any>): string {
switch (intent) {
case "create-test":
return \`Generate Playwright test for: \${metadata.featureDescription}\`;
case "generate-pr-summary":
return \`Summarize this pull request with title: \${metadata.prTitle}\`;
default:
return \`Unknown intent\`;
}
}

🧪 Real-World Use Case: AutoQA via MCP Server

Imagine you're building an autonomous agent to generate end-to-end Playwright tests from plain English.

Steps:

  1. User logs into test environment (auth token is passed to MCP).
  2. MCP fetches DOM snapshot + routes to proper Playwright test agent.
  3. Agent generates test code and stores it.
  4. MCP updates context with success/failure and reports back.

IMPORTANT: We ensured session handling in the MCP Server is secure by leveraging JWTs and client-side signed cookies.


🧱 Deployment & Scaling

You can deploy the MCP Server as:

  • Kubernetes service (for horizontal scaling)
  • Edge function (for prompt-sensitive latency)
  • Monorepo package for microservice orchestration

Use message queues (like RabbitMQ or Kafka) for async orchestration when chaining multiple AI agents.

kubectl apply -f mcp-server-deployment.yaml

TIP: Use OpenTelemetry to trace prompt → LLM → response cycles across your stack.


📊 Logging, Metrics, and Observability

Track:

  • Prompt generation latency
  • LLM response quality (BLEU, ROUGE, cosine similarity)
  • Context hits/misses

Example Prometheus metrics:

mcp_prompt_latency_seconds{intent="create-test"} 0.842
mcp_context_cache_hits_total 1203

Visualize with Grafana dashboards or export to DataDog for centralized tracing.


💡 Extending the MCP Server

Once you're up and running, here’s how to extend your MCP Server:

FeatureExtension Ideas
Context StoreAdd RAG or vector embeddings support
Model SelectorAdd multi-LLM routing (e.g., OpenAI vs Claude)
User PersonalizationStore user tone, preferred style, etc.
Multi-agent RoutingTrigger agents in sequence or parallel

TIP: MCP Server is perfect for chaining GenAI agents (e.g., test-gen → doc-gen → PR summary).


✅ Summary

The Model Context Protocol (MCP) Server helps you build scalable, multi-turn, and context-aware GenAI applications. Whether you're automating testing, document generation, or customer support bots — MCP will act as your memory layer and intelligent router.


📣 Call to Action

Happy hacking! 🚀