MCP server implementation sits at the boundary between AI clients, external systems, and user trust controls, so it is as much protocol engineering as it is application development. In practice, a solid MCP server is defined less by how many tools it exposes and more by how cleanly it negotiates capabilities, constrains state, validates input, and handles transport and authorization boundaries. The key mental model is to treat tools, resources, prompts, roots, sampling, and elicitation as separate surfaces with different control planes and risk profiles, ownership models, and approval expectations. A production-quality server also needs a precise grasp of JSON-RPC request semantics, session lifecycle, debugging signals, and client-side coordination rules, because many real failures come from mismatched assumptions rather than broken business logic. Once that separation is clear, choices like stdio vs Streamable HTTP, stateless vs stateful handlers, and OAuth vs local-only execution become much easier to design well.