4 posts tagged with "mcp"

AI coding agents waste a remarkable number of tokens just figuring out how your code connects. They open file after file, read imports, chase type definitions, and slowly build a mental model of architecture — burning through context windows and money in the process. @ttsc/graph takes a different approach: it hands the agent a compiler-resolved architecture graph upfront, so it can navigate by structure instead of reading source. It's early (v0.16), requires TypeScript v7 rc, and the benchmarks are vendor-supplied — but the idea is sharp enough to be worth knowing about. The author is Jeongho Nam (samchon), known for typia and nestia, which adds some credibility to the execution.

The Model Context Protocol specification release candidate (locked May 21, stable July 28, 2026) makes the protocol fully stateless — no handshakes, no session IDs, no sticky routing. Alongside that headline change, it graduates the extensions framework to first-class status, introduces MCP Apps for server-rendered UI, formalizes the Tasks extension for long-running work, adds production-grade observability headers, upgrades to full JSON Schema 2020-12, and hardens OAuth 2.0 authorization through multiple SEPs. Three features — Roots, Sampling, and Logging — are deprecated under a new lifecycle policy with a one-year sunset window.

AI coding tools are great at boilerplate, but they still hallucinate web API details, browser support, and even whether a feature exists at all. MDN's new MCP Server tackles that problem by letting MCP-compatible editors and agents pull current MDN documentation and browser compatibility data straight from the source.

When your product is an AI assistant, using AI to build it feels natural — but doing it well is harder than it sounds. The AI-Assisted Development section describes these patterns in the abstract. This post is what they look like after a year in production on a real Aliz frontend: a React + TypeScript chat-based AI assistant with theming, 18-language internationalization, MCP integrations, and multi-environment deploys. The codebase is large enough that no single prompt can reason about it coherently, which is the whole reason the team stopped reaching for a tool and started building a system — the same shape described in Multi-Agent Orchestration. Three layers of AI setup, a team of specialist agents, and a workflow called QRSPI hold it together.