Curated Reads

Source: “You Said No MCP!” — Earendil Author: Earendil Engineering · Originally published: September 29, 2026 Reprint note: The English article below is reproduced from the supplied PDF, with its original wording and links retained. The session replay has been completed using the supplied transcript; visit the source for the interactive replay. Statements about product behavior reflect the original publication date. If you went to pi.dev in the past, you found a proud declaration that Pi does not support MCP. If you listen to podcasts where we talked about Pi, you will have found more than one dismissive statement about MCP from us. Including a post by Mario about it. And yet, if you upgrade to Pi you will find MCP is now a supported piece of functionality. What happened? ...

Source: OpenAI API documentation — Programmatic Tool Calling Editorial note: This independently published companion preserves the guide’s technical structure and key examples in a condensed form. The official documentation is the canonical and most current reference. Programmatic Tool Calling lets a model write and run JavaScript that coordinates its available tools. Instead of returning every intermediate result to the model, a generated program can run calls concurrently, apply loops and conditions, combine results, and emit a smaller final value. ...

Source: OpenAI API documentation — Tool search Editorial note: This is an independently published companion to the official guide. It preserves the technical structure and key examples in a condensed form. The official documentation remains the canonical and most current reference. As an agent’s tool catalog grows, loading every definition into the model context becomes expensive. Parameter schemas consume input tokens even when the associated tools are irrelevant to the current task, and changing those definitions can invalidate a cached prompt prefix. ...

Source: Anthropic Engineering — Demystifying evals for AI agents Good evaluations help teams ship AI agents more confidently. Without them, it’s easy to get stuck in reactive loops—catching issues only in production, where fixing one failure creates others. Evals make problems and behavioral changes visible before they affect users, and their value compounds over the lifecycle of an agent. ...