14 posts tagged with "mcp"

The "USB-C for AI" analogy is catchy. It's also wrong in the ways that matter most when you're the one responsible for keeping it running in production. The Model Context Protocol solves a real problem—the explosion of custom N×M integrations between AI models and external systems—but the gap between "it works in the demo" and "it handles Monday morning traffic without leaking data or melting your latency budget" is wider than most teams expect.

MCP saw an 8,000% growth in server downloads in the five months after its November 2024 launch, with 97 million monthly SDK downloads by April 2025. That adoption speed is both a sign of genuine utility and a warning: most of those servers went into production without the teams fully understanding what they were building on.

Most AI agents are expensive because of a subtle architectural mistake: they treat every intermediate result as a message to be fed back into the model. Each tool call becomes a round trip through the LLM's context window, and by the time a moderately complex task completes, you've paid to process the same data five, ten, maybe twenty times. A single 2-hour sales transcript passed between three analysis tools might cost you 50,000 tokens — not for the analysis, just for the routing.

There's a better way. When agents write and execute code rather than calling tools one at a time, intermediate results stay in the execution environment, not the context window. The model sees summaries and filtered outputs, not raw data. The difference isn't incremental — it's been measured at 98–99% token reductions on real workloads.