A product discussed on AI Engineer.

MCP = Mega Context Problem - Matt Carey
Apr 25, 2026 · 22:42
Matt Carey from Cloudflare argues that the real bottleneck in connecting agents to APIs via MCP is the context window, not the protocol itself. Cloudflare’s OpenAPI spec is 2.3 million tokens; naive tool generation would consume 1.1 million, overwhelming any agent. To solve this, Carey advocates progressive discovery: CLIs (requiring shell access), tool search (loading only relevant tools), or codemode—letting agents write code against a typed SDK and execute it in Cloudflare’s isolated Workers sandbox. This sandbox provides safe, programmable guardrails (no secrets, limited network access), allowing agents to access all 2,600+ Cloudflare API endpoints from a single MCP server. Carey predicts that as agents become code generators, infrastructure primitives like WorkerD, Deno, and Pydantic will proliferate, and MCP servers will become a lightweight middleware flag (e.g., `MCP: true` in Next.js). The talk demonstrates read-only access to the entire Cloudflare API from an MCP client, showing codemode in action.

Remote MCPs: What we learned from shipping — John Welsh, Anthropic
Jun 19, 2025 · 15:12
John Welsh, a software engineer at Anthropic, explains how his team standardized on the Model Context Protocol (MCP) for all internal and external integrations, treating it as a universal JSON-RPC stream that can be piped through any transport—including web sockets, gRPC, or even email. He argues that adopting MCP internally solves integration chaos by providing a single, well-defined interface for tool calling, resource access, and authentication, and that building an "MCP gateway" as shared infrastructure creates a pit of success where engineers naturally follow the correct pattern. Welsh details how the gateway handles OAuth flows, credential portability for batch jobs, rate limiting, and observability, while also enabling future features like sampling and prompt injection defense at no extra cost. The talk emphasizes that MCP's message format, not the transport layer, delivers the most value, and that standardizing on any protocol—preferably MCP—reduces engineering overhead and allows teams to focus on high-leverage work.
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