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The Model Context Protocol (MCP) serves as a standard interface between large language models (LLMs) and external systems like PostgreSQL, emphasizing the importance of control over mere connectivity. An MCP server must act as a mediation and policy layer, enforcing security boundaries and ensuring that connections default to read-only. It should validate AI-generated SQL as untrusted input, encapsulate queries within transactions, and apply execution-time controls. The server must separate query generation from execution approval to manage operational costs and enforce guardrails on query types, such as limiting the number of rows returned. Token efficiency is crucial, with design considerations for compact data representation and schema introspection. In production, connection management is vital to prevent data leakage among multiple AI agents, and observability through logging executed queries and metadata is necessary for debugging and compliance. Long-running sessions require support for paginated responses to manage context effectively. Overall, the MCP server must integrate security, query safety, token efficiency, and observability into its design from the outset.