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CoursesAgentic Ai
Custom Tooling The "Model Context Protocol" (MCP)
Agentic Ai

Custom Tooling The "Model Context Protocol" (MCP)

Learn Custom Tooling & the Model Context Protocol (MCP) to enhance LLM workflows, enable context-aware AI, and build scalable, production-ready solutions.

5/5(4,890 Reviews)

Level

Advanced

Duration

8 Weeks

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About Custom Tooling The "Model Context Protocol" (MCP)

Custom Tooling & the Model Context Protocol (MCP) is a cutting-edge approach to enhance LLM workflows by enabling context-aware AI agents, seamless tool integration, and scalable automation. With MCP, developers can maintain structured conversation states, improve decision-making, and ensure reliable outputs in production environments. Tech Pratham offers hands-on training on MCP and custom tooling, equipping learners with practical skills to build enterprise-ready, context-driven AI solutions that maximize efficiency, accuracy, and automation.

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Training Plan

01
About trainer

About trainer

Working professional who is carrying more then 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.

Custom Tooling The "Model Context Protocol Course Curriculum

Introduction to Model Context Protocol (MCP)

Understand MCP fundamentals, its role in context-aware LLMs, and enterprise applications with Tech Pratham.

Overview of MCP and its significance in LLM workflows
Differences between traditional LLM usage and context-aware systems
Importance of MCP in enterprise-grade AI applications
Real-world use cases of context-driven AI agents

Foundations of Custom Tooling

Learn how custom tools enhance LLM workflows, enable automation, and improve decision-making in real-world scenarios.

Introduction to custom tooling for LLMs
Types of tools and integrations (APIs, databases, external services)
How custom tooling enhances AI decision-making and automation
Best practices for enterprise LLM tooling

MCP Architecture and Workflow

Master context management, memory, and session state design for structured, reliable LLM workflows.

Core components of MCP: context management, memory, and session states
Structured conversation design and context persistence
Multi-step decision-making with context tracking
Integration with LLM pipelines and automation workflows

Building Context-Aware Agents

Design intelligent, context-driven agents with tool integration, multi-step workflows, and error-handling strategies.

Designing agents with MCP for dynamic, real-time context handling
Implementing custom logic and tool calls
Handling complex workflows with multiple agents
Error handling, retries, and fallback strategies

Enterprise Integration with Custom Tools

Connect MCP-enabled agents with enterprise systems like ERPs, CRMs, and APIs for scalable automation.

Connecting MCP-enabled agents to ERP, CRM, and internal tools
Real-world enterprise workflow automation
API orchestration, tool validation, and output structuring
Security and governance best practices for enterprise LLMs

Monitoring & Production Reliability

Implement logging, monitoring, and troubleshooting to maintain reliable and observable MCP-driven AI agents.

Implementing logging and monitoring for context-aware agents
Detecting anomalies and maintaining output reliability
Metrics and observability for MCP workflows
Troubleshooting production issues in enterprise AI systems

Agentic Ai Courses

No related courses found

Additional Program Highlights

Learning Materials

Comprehensive study materials and resources

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Resume Writing

Professional resume building session

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Interview Preparation

Master your interview skills

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Live Project Demo

Real-world project demonstrations

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Who Should get enroll in this Custom Tooling The "Model Context Protocol

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Job Roles After Completing Custom Tooling The "Model Context Protocol Training

AI Agent Engineer

Multi-Agent Orchestration Specialist

LLMOps 2.0 / AgentOps Engineer

Key Projects

Custom Tooling The "Model Context Protocol

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

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Interview Preparation

Live Projects

What is the Model Context Protocol (MCP)?

Why is custom tooling important for LLMs?

Who should enroll in this MCP course?

How does MCP improve LLM reliability?

What skills will learners gain from studying MCP?

Does the course cover context management in LLMs?

What is the core role of an MCP server in an app?

How does MCP eliminate model lock-in for AI tools?

When should you choose SSE over stdio for transport?

Explain the difference between a Tool and a Resource.

What protocol handles communication in MCP infrastructure?

How does an MCP client know what tools are available?

Custom Tooling The "Model Context Protocol Certification

The Custom Tooling and Model Context Protocol (MCP) module is an intensive, industry-aligned training track running for 3 to 4 weeks (comprising 20+ hours of live engineering workshops and hands-on builds) designed to master the open-source standard for connecting LLMs to real-world infrastructure. The curriculum breaks down the traditional, fragmented approach to tool calling by teaching a unified, decentralized client-server architecture where AI models can effortlessly discover, read, and write data across diverse environments without model lock-in. Engineers gain critical production-ready benefits, including the ability to build reusable tool suites that integrate natively with modern IDEs like Cursor and Claude Desktop, while establishing zero-trust data security architectures that wrap sensitive enterprise databases and internal web APIs safely behind local corporate firewalls.

To certify your proficiency as an MCP developer, you will undergo a timed, 120-minute remotely proctored final evaluation at the end of the module track that carries a 50% grading weightage. This comprehensive exam measures protocol-compliant software design through multi-select conceptual routing questions, JSON-RPC schema verification tasks, and backend code-snippet challenges focusing on client-server handshake routines, custom transport layer configurations (stdio/WebSockets), and primitive error handling. The remaining 50% of your score is determined by a practical laboratory verification where your custom-built MCP server is actively deployed and evaluated against a dynamic test suite to ensure its scalability, schema accuracy, and operational security before receiving your certification module credit.

Industry-Recognized Certification

Certificate
Custom Tooling The "Model Context Protocol Program

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