Building Your First MCP Server and Client

Module 1: Introduction to MCP and Agent-Facing Design

• Examine the integration problem MCP is designed to solve and how it standardizes connections between AI applications and external tools, resources, and workflows.
• Review MCP architecture, including the roles of the host, client, and server.
• Explore how context windows affect tool design and output design.
• Identify the three main server primitives: tools, resources, and prompts.

Module 2: Why Many MCP Servers Perform Poorly

• Analyze why MCP servers should not be designed as one-to-one REST wrappers and the cost of excessive tool discovery for agents.
• Evaluate why agent iteration is slower and more expensive than human trial and error.
• Assess how output format and schema size affect context efficiency.
• Apply principles of outcome-based tool design to improve server performance.

Module 3: Build an MCP Server with FastMCP

• Initialize a new FastMCP project, create a simple tool, and inspect the server with the MCP Inspector.
• Review the SpaceX API and identify likely user workflows for tool design.
• Build a naive version to observe anti-patterns, then refactor into a smaller, more effective toolset.
• Format outputs for LLM consumption using Markdown and improve tool descriptions, matching logic, and error handling.

Module 4: Build a Minimal MCP Client

• Connect to the server from a local client, discover tools via tools/list, and call tools programmatically.
• Examine the handshake and tool-call lifecycle between client and server.
• Observe how an LLM-driven host uses the same pattern to interact with MCP servers.

Module 5: Deployment, Security, and Ecosystem Overview

• Transition from local stdio development to remote deployment, including remote transport and hosting basics.
• Review security and authorization considerations for remote MCP servers.
• Survey the broader MCP ecosystem, including documentation, SDKs, and registries.