MCP enables an AI to understand which tools rest at its disposal : web search, file editing, & email drafting for example.
Ten months later, we analyzed 200 MCP tools to understand which categories developers actually use.
Three usage patterns have emerged from the data :
Development infrastructure tools dominate with 54% of all sessions despite being just half the available servers. Terminal access, code generation, & infrastructure access are the most popular.
While coding, engineers benefit from the ability to push to GitHub, run code in a terminal, & spin up databases. These tools streamline workflows & reduce context switching.
Information retrieval captures 28% of sessions with fewer tools, showing high efficiency. Web search, knowledge bases, & document retrieval are key players. These systems are likely used more in production, on behalf on users, than during development.
Everything else including entertainment, personal management, content creation, splits the remaining 18%. Movie recommenders, task managers, & Formula 1 schedules fill specific niches.
MCP adoption is still early. Not all AIs support MCP. Of those that do, Claude, Claude Code, Cursor top the list (alliteration in AI). Developer focused products & early technical adopters are the majority of users.
But as consumer use of AI tools grows & MCP support broadens, we should expect to see a much greater diversity of tool use.