- MCP analytics is product analytics for your MCP server.
- It answers who connects, what they call, and whether it worked.
- It installs at the protocol layer in one line.
MCP analytics is the practice of measuring how AI clients actually use your MCP server: which clients connect, which tools they call, whether the work completes, and where it fails. It is product analytics for the Model Context Protocol, the same way Google Analytics is product analytics for a website.
The one-line definition
MCP analytics turns raw tool-call traffic into who, what, and whether it worked.
What it measures
- Clients: which agents connect and how usage grows
- Tools: call volume, adoption, and the tools nobody uses
- Workflows: the paths agents take and where they stop
- Outcomes: completion rate and returning usage
- Reliability: errors, including failures hidden inside a 200 OK
How it differs from logging
Logs record that a request happened. Analytics reconstructs behavior: it groups calls into sessions, attributes them to clients, and measures whether a task actually finished. Logs answer 'did it run?' Analytics answers 'did it work, for whom, and what should I fix?'
Do you need it
If you have shipped an MCP server and cannot answer which tool is most used or which one silently fails, you need MCP analytics. It installs at the protocol layer in one line and starts answering those questions from the first call.
See this on your own server
TrackMCP turns your MCP server's calls into adoption, workflows, and outcomes. One line to install.