TrackMCP
AIAnalytics for MCP servers — see which AI clients connect, which tools they call, whether the work completes and what to fix.
TrackMCP
Analytics for MCP servers — see which AI clients connect, which tools they call, whether the work completes and what to fix.
About TrackMCP
TrackMCP is a usage-analytics and observability service built specifically for Model Context Protocol servers. You add one line to your server using the official TypeScript or Python SDK, and the dashboard begins showing which AI clients are connecting — Claude, Cursor, ChatGPT or custom agents — how many are new, and how many come back. Rather than stopping at raw call counts, it reconstructs workflows: the path from a client's first request to a useful result, which tools are used in what order, and where sessions stop early. Its reliability view is aimed at a failure mode unique to agent tooling: calls that return a visible 200 OK while carrying isError, causing the agent to retry and eventually give up. TrackMCP surfaces those stalls, groups them by tool, reports error rates and average retries, and names the likely cause such as a schema mismatch. No manual event tagging is required and data appears in the dashboard in real time.
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Key Features
Use Cases
Frequently asked questions about TrackMCP
What is TrackMCP?
Analytics for MCP servers — see which AI clients connect, which tools they call, whether the work completes and what to fix.
How does TrackMCP work?
TrackMCP works by combining One-line install: Drop the @trackmcp/sdk into an existing TypeScript or Python MCP server with no manual event tagging, Client breakdown: See the share of traffic coming from Claude, Cursor, ChatGPT and custom agents, Tool analytics: Per-tool call volume, adoption, latency percentiles and health status ranked in one table, Workflow paths: Follow sessions from first request to result and see exactly where they stop, Outcome tracking: Completion rates, sessions that reached a tool and returning clients within seven days to help users with An MCP server author finds out which of their tools agents actually call and which have never been used, A team diagnoses why a checkout workflow stops at 38% instead of completing, by replaying the session path, A maintainer catches a tool failing 94% of calls behind a 200 OK response that logs never surfaced, A product team measures whether new clients keep coming back within seven days of first connecting, An engineer compares latency and error rates across production and staging before shipping a schema change.
What are the main features of TrackMCP?
Key features include One-line install: Drop the @trackmcp/sdk into an existing TypeScript or Python MCP server with no manual event tagging, Client breakdown: See the share of traffic coming from Claude, Cursor, ChatGPT and custom agents, Tool analytics: Per-tool call volume, adoption, latency percentiles and health status ranked in one table, Workflow paths: Follow sessions from first request to result and see exactly where they stop, Outcome tracking: Completion rates, sessions that reached a tool and returning clients within seven days.
Who is TrackMCP for?
TrackMCP is useful for anyone interested in An MCP server author finds out which of their tools agents actually call and which have never been used, A team diagnoses why a checkout workflow stops at 38% instead of completing, by replaying the session path, A maintainer catches a tool failing 94% of calls behind a 200 OK response that logs never surfaced, A product team measures whether new clients keep coming back within seven days of first connecting, An engineer compares latency and error rates across production and staging before shipping a schema change.
How much does TrackMCP cost?
TrackMCP offers a free tier with paid plans for advanced features.
How do I get started with TrackMCP?
Visit https://www.trackmcp.com/ to sign up and explore TrackMCP.
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