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A guide to learning how MCP registries help govern AI agent-to-tool connectivity AI agents are only as capable as the tools they can reach. When an agent needs to query a database, file a support ticket, or pull data from a CRM, it has to find the right tool, authenticate, and invoke it — all at runtime. The Model Context Protocol (MCP) standardizes how agents communicate with these tools. But MCP alone does not answer a fundamental question: how does the agent know which tools exist?

Last updated: May 2026 A PostgreSQL MCP server is a service that exposes PostgreSQL databases as tools an AI agent can call through the Model Context Protocol (MCP). Rather than giving an LLM direct database credentials, you put an MCP server between the agent and the database. The agent discovers what queries it can run, calls them as named tools, and the MCP server translates those calls into safe, governed SQL against PostgreSQL.

The ‘AI Credit’ Economy: GitHub’s Pricing Shift Is the Beginning, Not the Exception *GitHub just sent waves of budget panic across its developer base. Seat-based Copilot pricing is out. Consumption-based credits are in. And if you're building an AI-driven product today on flat-rate pricing? You're building a problem into your roadmap.* Seats aren't going away, but they now fund a shared pool of AI credits (one credit = one cent) instead of unlocking uncapped use.

Your team ships a UI update on Monday. By Tuesday morning, 47 automated tests are failing and half of them are not real bugs. They broke because a button ID changed from confirmButton to confirm-purchase-btn. Your engineers spend hours figuring out what is an actual regression and what is just a broken locator. Self healing test automation solves this by allowing tests to automatically recover from UI changes, locator failures, timing issues, and API schema updates without constant manual fixes.