Building Content and Context Systems for AI
The Model Context Protocol (MCP) ecosystem has exploded. In just months, developers have published hundreds of servers and thousands of tools. It’s a remarkable show of creativity: everything from lightweight utilities that return the time of day to heavyweight servers that can administer databases, manage cloud infrastructure, or even control a computer.
We analyzed 1,945 tools across 238 servers in the MCP Registry to understand how tools are distributed.
This week, we’re launching a new phase of research at Lyr3: digging into the tools that power the MCP ecosystem.
So far:
We’ve collected data on > 420 MCP servers.
Across ~300 of them, we’ve identified 1,400+ distinct tools, tool descriptions and inputs.
MCP continues to expand its reach across enterprise and developer ecosystems, with new use cases, platform adoption, and benchmarks revealing both promise and challenges.
Chat boxes have become the face of AI. We type, the AI replies, and when tools are connected through MCP (Model Context Protocol), the outputs are poured back into text. Clean, minimal plumbing.
But MCP-Universe, a benchmarking project, shows just how brittle that model is. Only about one-third of complex interactions succeed when the AI has to juggle multi-step orchestration entirely through chat. That raises a question: are we overloading the AI, and forcing the chat box to do too much?
When people hear “Model Context Protocol (MCP),” it sounds abstract and technical—like plumbing for AI systems. But if you strip it down, MCPs are really just goal-oriented choose-your-own-adventure games written for AI.
MDN (Markdown Notation) project defining a simple, AI-readable spec for representing tabular and contextual data for AI systems