MCP (Model Context Protocol) for AI Applicationsin Seattle

40 Hours/5 daysLive virtual trainingIntermediate

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Seattle, United States. Dates, seating capacity, facilities and the final venue are confirmed before booking.

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Seattle is known for diverse industries, professional networks and internationally connected business communities.

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Explore MCP (Model Context Protocol) for AI Applications

Explore MCP (Model Context Protocol) for AI Applications and learn to connect AI assistants with external tools, APIs, and data sources. Develop practical skills in MCP architecture, client-server communication, tool integration, resource handling, and secure AI workflows. Through hands-on exercises, discover how MCP simplifies AI application development and improves interoperability. Join StepMerit's MCP training in Seattle, United States, to strengthen your Generative AI development skills and build intelligent, connected AI applications.

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Learning from Seattle

Seattle is known for diverse industries, professional networks and internationally connected business communities.

Course curriculum

Module 1: Introduction to MCP (Model Context Protocol)
- Understanding Model Context Protocol and its purpose.
- Evolution of AI application integration.
- Benefits and use cases of MCP.
- Introduction to MCP training with StepMerit in Seattle, United States.
Module 2: Generative AI and MCP Fundamentals
- Overview of Large Language Models (LLMs) and AI assistants.
- Understanding tool calling and contextual information.
- Role of MCP in AI application development.
- Comparing traditional integrations with MCP-based approaches.
Module 3: MCP Architecture and Core Components
- Understanding MCP hosts, clients, and servers.
- Communication between clients and servers.
- Overview of tools, resources, and prompts.
- Understanding MCP capabilities and interaction patterns.
Module 4: Development Environment Setup
- Installing Python or Node.js development tools.
- Configuring Visual Studio Code and terminal environments.
- Managing packages, dependencies, and environment variables.
- Preparing a project structure for MCP development.
Module 5: MCP Protocol and Communication
- Understanding JSON-RPC-based communication.
- Exploring requests, responses, notifications, and errors.
- Learning capability negotiation and protocol lifecycle concepts.
- Understanding supported transport mechanisms.
Module 6: Building an MCP Server
- Creating a basic MCP server using a supported SDK.
- Defining server capabilities and metadata.
- Registering tools, resources, and prompts.
- Running and testing a local MCP server.
Module 7: Developing MCP Clients
- Understanding MCP client responsibilities.
- Connecting AI applications to MCP servers.
- Discovering available server capabilities.
- Handling connection errors and server responses.
Module 8: MCP Tools and Function Execution
- Defining tools with names, descriptions, and input schemas.
- Validating tool arguments and processing results.
- Integrating tools with approved APIs and application functions.
- Handling execution failures and unsupported requests.
Module 9: MCP Resources and Context Management
- Understanding MCP resources and resource identifiers.
- Exposing documents and structured information.
- Retrieving contextual information for AI applications.
- Managing resource access and data validation.
Module 10: MCP Prompts and Reusable Templates
- Creating reusable prompt templates.
- Defining prompt arguments and expected inputs.
- Supporting consistent AI interaction patterns.
- Testing prompts with different application scenarios.
Module 11: Integrating MCP with LLM Applications
- Connecting MCP clients to AI application workflows.
- Using compatible foundation models and AI frameworks.
- Enabling model-driven tool selection and invocation.
- Processing tool outputs into useful AI responses.
Module 12: MCP with APIs, Databases, and External Services
- Connecting MCP servers to REST APIs.
- Retrieving information from databases and approved data sources.
- Integrating business applications and internal services.
- Managing credentials and external service errors securely.
Module 13: MCP Transport and Remote Connectivity
- Understanding standard input/output (stdio) transport.
- Exploring Streamable HTTP for supported remote deployments.
- Configuring connections between remote clients and servers.
- Understanding authentication and network security considerations.
Module 14: Security, Testing, and Troubleshooting
- Applying authentication and authorisation controls where required.
- Validating tool inputs and limiting access to sensitive operations.
- Addressing prompt injection and untrusted tool outputs.
- Testing server capabilities, logging errors, and diagnosing failures.
Module 15: Capstone Project and Deployment
- Designing an AI assistant that retrieves information and invokes approved tools.
- Building an MCP server connected to a sample API or knowledge source.
- Integrating an MCP client with an AI application.
- Testing end-to-end functionality, security, and error handling.
- Documenting the architecture and demonstrating the completed project.

Exam details

Exam Level: Beginner to Intermediate – MCP for AI Applications
Exam Type: Online computer-based examination consisting of multiple-choice questions (MCQs) and scenario-based questions.
Number of Questions: 40 questions.
Exam Duration: 90 minutes.
Passing Score: 70% (28 out of 40 questions).
Exam Mode: Online assessment through the StepMerit learning and assessment platform.
Practical Assessment: May include hands-on tasks involving MCP server configuration, tool integration, client connectivity, or AI application troubleshooting.
Evaluation Criteria: Assessment of conceptual understanding, practical implementation, integration skills, and problem-solving abilities.
Certification: Successful candidates may receive a StepMerit course completion certificate, subject to the applicable assessment and certification policy. This is not an official third-party MCP certification.
Preparation Guidelines: Review the course materials, practise building MCP clients and servers, and complete hands-on integration exercises.
Training Support: StepMerit offers MCP (Model Context Protocol) for AI Applications training in Seattle, United States, with practical learning activities to support skill development.

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Who this course is for

AI Application Developers Software Developers Generative AI Engineers Python Developers API Integration Specialists Cloud and DevOps Engineers IT Security Professionals Solution Architects Data Engineers Students, Graduates, and AI Enthusiasts in Seattle, United States

Before you begin

Basic Programming Knowledge: Familiarity with Python, JavaScript, or another programming language is recommended. API Fundamentals: Understanding REST APIs, HTTP requests, JSON, and client-server communication is helpful. Generative AI Fundamentals: Basic knowledge of Large Language Models (LLMs), AI assistants, and prompt engineering is beneficial. Basic Software Development: Familiarity with functions, modules, dependencies, and application development concepts is recommended. Development Tools: Basic experience using Visual Studio Code or another code editor, a terminal, and package managers is useful. JSON and Data Handling: Understanding structured data formats and exchanging…

Learning outcomes

Problem Statement An organisation wants to implement an AI-powered assistant to help its IT security and governance teams access security policies, compliance documents, incident response procedures, and access control guidelines. These documents are distributed across multiple systems, making information retrieval time-consuming and inefficient. Connecting AI applications directly to enterprise systems also introduces risks, including unauthorised data access, unsafe tool execution, prompt injection, and insufficient audit logging. The organisation needs a solution that enables AI applications to interact with approved enterprise resources while maintaining security, governance, and…

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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Professional Experience: 5 years in software development, AI technologies, and application integration. Training Experience: 100+ hours of technical training in AI application development, API integration, and related technologies. Candidates Trained: 150+ candidates across technical training programs and practical workshops. MCP Knowledge: Training coverage includes MCP architecture, clients, servers, tools, resources, and prompts. AI Application Integration: Practical guidance on connecting AI assistants with APIs, databases, and external services using MCP. Security and Governance: Covers access control, input validation, secure tool execution, and audit logging fundamentals. Hands-On…

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Insights for MCP (Model Context Protocol) for AI Applications

Learning insight 01

MCP (Model Context Protocol) for AI Applications: A Complete Guide to AI Integration

Generative AI is transforming how businesses develop applications, automate workflows, and interact with data. However, connecting AI assistants with external tools, databases, APIs, and enterprise systems can be complex. Model Context Protocol (MCP) provides a standardised way for AI applications to communicate with…

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