Getting Started with Model Context Protocol (MCP)
Chapter 1: Architecture and Fundamentals
The MCP Specification Overview
Client-Host-Server Topology
JSON-RPC Message Structure
Transport Mechanisms: Stdio and SSE
Capabilities Negotiation
Hands-on Practice: Environment Setup
Chapter 2: Defining Resources and Prompts
Concept of Resources in MCP
URI Schemes and Patterns
Implementing Synchronous Resource Reads
Resource Subscriptions and Notifications
Structuring Prompts
Hands-on Practice: Building a File Reader Server
Chapter 3: Implementing Tools and Logic
Tool Definition Schema
Input Validation with Pydantic
Executing External API Calls
Error Handling and Reporting
Designing Computed Resources
Hands-on Practice: Creating a Database Tool
Chapter 4: Debugging and Client Integration
Using the MCP Inspector
Analyzing Log Output
Configuration for Claude Desktop
Managing Environment Variables
Handling Connection Lifecycles
Hands-on Practice: End-to-End Integration
Structuring Prompts
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- Prompt engineering, OpenAI, 2024 - Provides practical guidelines and strategies for designing effective prompts, covering techniques to optimize large language model outputs and interaction patterns.
- Function calling, OpenAI, 2024 - Explains how to integrate large language models with external tools and APIs, detailing the process of defining callable functions and parsing structured arguments for models.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Donghao Xu, Zhezhang Ding, Xu He, Huijing Zhao, Mathieu Moze, François Aioun, Franck Guillemard, 2020 arXiv preprint arXiv:2005.11470 DOI: 10.48550/arXiv.2005.11470 - Introduces the Retrieval-Augmented Generation (RAG) framework, demonstrating how to enhance large language models by incorporating external knowledge bases to improve factual consistency and reduce hallucinations.
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