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Learning by doing
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Trainers with practical experience
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Classroom training
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Detailed course material
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Clear content description
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Tailormade content possible
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Training that proceeds
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Small groups
The course Model Context Protocol from SpiralTrain teaches you how to build AI applications that seamlessly connect to external data sources and tools using the standardized Model Context Protocol. You will learn how to implement MCP servers and clients, integrate contextual information into AI workflows, and create extensible applications that leverage real-world data effectively.
The course Model Context Protocol starts with an overview of the protocol including the AI context problem, JSON-RPC foundation, transport mechanisms, resource concepts, tool definitions, and real-world use case examples.
Next the architecture patterns are explored, covering communication flow, message types, capability negotiation, session management, error handling, connection lifecycle, protocol extensions, and versioning strategies for robust implementations.
This module covers building MCP servers including SDK installation, implementing resource providers, developing and registering tools, context management, data source integration, authentication methods, configuration options, and comprehensive testing approaches.
Here participants learn to build MCP clients including SDK integration, connection management, server discovery, resource access patterns, tool invocation, prompt usage, response handling, state management, and error recovery mechanisms.
This part focuses on integrating MCP with LLMs such as Claude and OpenAI. Topics include context injection, multi-server setups, fallback strategies, caching mechanisms, rate limiting, and monitoring solutions.
The course concludes with production deployment strategies including containerization, scaling solutions, load balancing, security hardening, API gateway setup, version management, documentation standards, testing strategies, monitoring tools, and hands-on production projects.
This course is intended for AI developers, and software architects who want to build AI applications that connect to external data sources and tools using the Model Context Protocol.
Participants should have solid programming experience in Python or TypeScript and understanding of API development.
The course combines theoretical sessions with hands-on labs guided by a trainer. Real-world case studies are central to the training experience.
After completion, participants receive a certificate of participation in Model Context Protocol.
Module 1: MCP Fundamentals |
Module 2: Protocol Architecture |
Module 3: Server Implementation |
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MCP Protocol Overview AI Context Problem Protocol Specifications JSON-RPC Foundation Transport Mechanisms Client-Server Model Resource Concepts Tool Definitions Prompt Templates Use Case Examples Security Basics |
Architecture Patterns Communication Flow Message Types Request-Response Cycle Capability Negotiation Session Management Error Handling Connection Lifecycle Protocol Extensions Versioning Strategy Best Practices |
Server Setup SDK Installation Resource Providers Implementing Resources Tool Development Tool Registration Context Management Data Source Integration Authentication Methods Configuration Options Testing Servers |
Module 4: Client Development |
Module 5: Integration Patterns |
Module 6: Production Deployment |
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Client Setup SDK Integration Connection Management Server Discovery Resource Access Tool Invocation Prompt Usage Response Handling State Management Error Recovery Performance Optimization |
LLM Integration Claude Integration OpenAI Integration Context Injection Multi-Server Setup Fallback Strategies Caching Mechanisms Rate Limiting Monitoring Solutions Logging Practices Real-World Examples |
Deployment Strategies Containerization Scaling Solutions Load Balancing Security Hardening API Gateway Setup Version Management Documentation Standards Testing Strategies Monitoring Tools Production Projects |
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