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MCP (Model Context Protocol)
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MCP (Model Context Protocol)

Learn Model Context Protocol to build MCP servers, clients, tools, resources, prompts, SDK-based integrations, and practical Generative AI applications.

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Level

Advanced

Duration

4 Weeks

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About MCP (Model Context Protocol)

MCP (Model Context Protocol) Course is a practical training program designed to help learners understand and build standardized integrations between AI applications and external tools, data, and services. The course covers MCP architecture, hosts, clients, servers, protocol communication, Tools, Resources, Prompts, SDKs, testing, integrations, security, authorization, and production-oriented application development. Learners build MCP servers and clients, expose tools and resources, connect AI applications with external APIs and data sources, and apply MCP concepts through practical projects. The course is designed to develop hands-on skills for modern Generative AI and AI integration development.

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Training Plan

01
About trainer

About trainer

Working professional who is carrying more than 10 years of industry experience.

02
Decks & Updated Content

Decks & Updated Content

Access to updated presentation decks shared during live training sessions.

03
e-Book

e-Book

E-book provided by TechPratham. All rights reserved.

04
Assignments & MCQs

Assignments & MCQs

Module-wise assignments and MCQs provided for practice.

05
Video Recording

Video Recording

Daily Session would be recorded and shared to the candidate.

06
Projects

Projects

Live projects will be provided for hands-on practice.

07
Resume Building

Resume Building

Expert-guided resume building with industry-focused content support.

08
Interview Preparation

Interview Preparation

Comprehensive interview preparation with real-time scenario practice.

MCP (Model Context Protocol) Course Curriculum

Module 1 — Introduction to Model Context Protocol

Understand Model Context Protocol, its purpose, ecosystem, use cases, and role in connecting AI applications with external tools, data, and services.

What is Model Context Protocol?
Why MCP was created
MCP use cases
MCP ecosystem
MCP terminology
MCP and Generative AI
AI application integrations
MCP adoption
MCP development overview

Module 2 — MCP Architecture & Core Concepts

Explore the core MCP architecture and understand how hosts, clients, and servers work together to provide AI applications with standardized capabilities.

MCP architecture
MCP Host
MCP Client
MCP Server
Client-server relationship
MCP capabilities
Capability discovery
Protocol lifecycle
MCP implementation model
Architecture patterns

Module 3 — MCP Protocol & Communication

Learn how MCP components communicate through protocol messages, requests, responses, notifications, transports, and structured communication patterns.

MCP messages
JSON-RPC concepts
Requests and responses
Notifications
Error handling
Capability negotiation
Transport concepts
Streamable HTTP
Local communication
Protocol communication flow

Module 4 — MCP Server Development

Learn how to create, configure, run, and test MCP servers that expose capabilities for AI applications and MCP clients.

MCP server fundamentals
Server setup
Server configuration
Server capabilities
Creating an MCP server
Registering capabilities
Local MCP servers
Remote MCP servers
Server lifecycle
Server testing

Module 5 — MCP Tools

Build MCP Tools that allow AI applications to discover and execute functions, interact with APIs, and perform controlled actions through MCP servers.

What are MCP Tools?
Tool definitions
Tool schemas
Tool discovery
Tool inputs
Tool outputs
Tool execution
Custom tools
API-based tools
Tool validation
Tool safety

Module 6 — MCP Resources & Prompts

Understand MCP Resources and Prompts and learn how they complement Tools to provide information, reusable instructions, and structured application capabilities.

MCP Resources
Resource discovery
Resource URIs
Resource templates
Reading resources
MCP Prompts
Prompt templates
Prompt arguments
Tools vs Resources vs Prompts
Practical use cases

Module 7 — MCP Clients & AI Application Integration

Learn how MCP clients connect AI applications to MCP servers and consume capabilities such as Tools, Resources, and Prompts.

MCP Client fundamentals
Client configuration
Connecting to MCP servers
Server discovery
Client capabilities
Multiple server connections
Host applications
Context integration
AI application integration
Client workflows

Module 8 — MCP SDK & Application Development

Develop practical MCP applications using SDKs and learn how to implement MCP servers, clients, tools, resources, and prompts using supported development approaches.

MCP SDK fundamentals
Python SDK
TypeScript SDK
SDK architecture
Server implementation
Client implementation
Tool implementation
Resource implementation
Prompt implementation
SDK testing

Module 9 — MCP Integration & AI Workflows

Apply MCP to practical AI integrations by connecting applications with APIs, databases, files, business systems, developer tools, and external services.

MCP integration patterns
API integration
Database integration
File and document integration
External service integration
Developer tool integration
Multiple MCP servers
AI workflows
Context-aware applications
Enterprise integration use cases

Module 10 — MCP Security & Authorization

Learn how to design safer MCP integrations by understanding authentication, authorization, user consent, data protection, tool security, and access-control considerations.

MCP security fundamentals
User consent
Security threats
Data privacy
Tool safety
Access control
Authentication
Authorization
OAuth concepts
Secure MCP server design

Module 11 — Advanced MCP Development

Explore advanced MCP capabilities and development patterns for building more sophisticated AI integrations, workflows, and applications.

MCP Tasks
Long-running operations
Elicitation
Sampling
Notifications
Progress tracking
Logging
Extensions
Advanced workflow patterns
MCP application patterns

Module 12 — Production MCP Applications

Apply MCP concepts to production-oriented applications while considering deployment, scalability, reliability, security, monitoring, testing, and enterprise integration requirements.

Production MCP architecture
Remote MCP deployment
Scalability
Reliability
Security
Monitoring
Performance optimization
Testing and evaluation
Enterprise MCP integrations
Capstone project

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Learning Materials

Comprehensive study materials and resources

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Professional resume building session

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Real-world project demonstrations

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Who Should Take MCP Course?

IT Professionals

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Career Opportunities After MCP Course

MCP Developer

MCP Server Developer

AI Integration Developer

Key Projects

MCP Course Projects

Microsoft

MicrosoftUnified AI Context Bridge


Scenario: Developed custom tooling using Model Context Protocol to standardize AI model communication secure context sharing and enterprise workflow orchestration.

Live Work:

  • Built MCP compliant connectors
  • Secured context exchange APIs
  • Integrated Azure AI services
Outcome: Enabled secure AI tool interoperability
Amazon Web Services

Amazon Web ServicesCloud Model Integration Grid


Scenario: Implemented MCP based framework to orchestrate multi model interactions automate context routing and ensure scalable cloud AI deployments.

Live Work:

  • Designed MCP routing logic
  • Automated model orchestration
  • Integrated AWS Lambda flows
Outcome: Optimized multi model performance
Google

GoogleIntelligent Context Fabric


Scenario: Executed custom MCP tooling to harmonize data pipelines enable real time AI context management and streamline developer workflows.

Live Work:

  • Developed context adapters
  • Managed real time pipelines
  • Secured cross model access
Outcome: Enhanced AI workflow scalability
Salesforce

SalesforceEnterprise AI Protocol Hub


Scenario: Led MCP implementation enabling CRM integrated AI assistants secure context persistence and enterprise grade automation controls.

Live Work:

  • Integrated MCP with CRM data
  • Configured secure context vault
  • Built automation triggers
Outcome: Strengthened AI driven CRM automation
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Our Success Mantra

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Commitment

  • Ensuring quality training every day

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Fulfillment

  • Meeting learning goals with confidence

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Accomplishment

  • Students achieving industry-ready expertise

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Beyond Courses:

Additional Support We Provide

24/7 Support

LinkedIn Profile

Resume Writing

Alumni Sessions

Interview Preparation

Live Projects

What is Model Context Protocol (MCP)?

What is an MCP Course?

What is an MCP Server?

What are MCP Tools?

What is an MCP Client?

What are MCP Resources?

What is Model Context Protocol?

What are the main components of MCP architecture?

What is an MCP Server?

What is an MCP Client?

What is the difference between MCP Tools, Resources, and Prompts?

How does an MCP client communicate with an MCP server?

MCP Certification

Upon successful completion of the course requirements, learners can receive a certificate recognizing their learning and practical understanding of Model Context Protocol, MCP architecture, servers, clients, Tools, Resources, Prompts, SDKs, integrations, security, and AI application development. The certification reflects successful completion of the applicable MCP course requirements and practical learning activities, subject to TechPratham's applicable certification criteria.

Industry-Recognized Certification

Certificate
MCP (Model Context Protocol)

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TechPratham Introduces Hire-Train-Deploy Model to Transform HR & ERP Talent in the AI Era
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