Tool analytics
Track tool calls, ranking, and trends over time so you can see which tools are used most and how adoption changes.
TrackMCP
TrackMCP is an analytics and observability tool for MCP servers. It helps teams see which clients and tools are being used, where workflows fail or stall, and what to improve next.
Overview
TrackMCP is an analytics and observability layer for MCP servers. It wraps an existing MCP server at the server boundary and turns protocol traffic into usage, reliability, and workflow signals without changing the tool implementations.
The product is designed to show who is using a server, which tools they call, where sessions stop, and what needs attention next. It supports TypeScript and Python SDKs, captures telemetry in real time, and is set up with a one-line wrapper and a workspace key.
Features
Latency and error tracking
See latency and error-rate profiles per tool, including p50 and p95 latency, to identify slow or flaky calls.
Silent-failure detection
Detect failures that appear as a successful HTTP 200 response but contain tool errors in the payload, including isError responses.
Call inspector
Open any call to inspect arguments, results, timing, client, and error details in one record.
Client and session analysis
Break usage down by client, replay sessions, and see where workflows stall or drop off before completion.
Actionable insights and integrations
Receive plain-English insights plus alerts and integrations such as email, Slack, webhooks, REST API access, CSV export, and OpenTelemetry export.
Use Cases
Monitor client adoption
Use TrackMCP to see which AI clients connect to your server, how usage is distributed across Claude, Cursor, ChatGPT, and custom agents, and whether returning usage is growing.
Diagnose reliability issues
Use the latency, error, and silent-failure views to find tools that are slow, flaky, or returning tool errors inside otherwise successful responses.
Trace workflow drop-off
Replay sessions and inspect calls to understand where users or agents stop in a workflow, then identify the tool or schema issue that caused the drop-off.
Audit tool usage
Use the analytics and zero-call tool views to identify low-value tools, dead tools, and schema areas that may be unused or misconfigured.
Operationalize telemetry
Use alerts, webhooks, REST access, and exports to feed MCP telemetry into existing operational workflows and external systems.
Pros and Cons
Pros
- Adds observability without changing tool implementations.
- Captures tool calls, sessions, clients, errors, latency, and workflow outcomes from the server boundary.
- Surfaces silent failures inside successful 200 OK responses, which basic logs can miss.
- Includes plain-English insights and suggested fixes rather than only raw charts.
- Offers multiple integration paths, including Slack, webhooks, REST API, CSV export, and OpenTelemetry export.
Cons
- The source is strongest on server-side analytics and observability; it provides less detail about downstream destinations and custom workflows than about the dashboard itself.
- Enterprise deployment, retention, and security terms are custom, so exact limits and delivery options are not specified publicly.
FAQ
How does TrackMCP get data from my MCP server?
TrackMCP wraps an existing MCP server at the server boundary and records tool calls, sessions, clients, errors, and outcomes. The docs say data appears in the dashboard after the first telemetry flush and a real tool call.
Which MCP server runtimes does TrackMCP support?
Yes. The pricing page lists both TypeScript and Python SDK support, and the docs show a one-line wrapper for each.
Do I need to change my tools to use TrackMCP?
No. The source says you wrap your existing MCP server once with the SDK and keep your tool implementations unchanged.
What pricing options are available?
TrackMCP offers a free Hobby plan, a paid Pro plan, and an Enterprise plan with custom pricing. The Hobby plan includes 1,000 captured tool calls per month, while Pro includes 50,000 captured tool calls per month.
Where is telemetry stored?
The pricing page says telemetry is stored in managed Postgres infrastructure, and Enterprise customers can discuss retention, export, and deployment requirements before signing.
Quick Facts
- Category
- Developer Tool
- Product type
- Analytics and observability for MCP servers
- Supported SDKs
- TypeScript and Python
- Installation
- One-line server wrapper with @trackmcp/sdk
- Pricing
- Free Hobby plan, paid Pro plan, custom Enterprise plan
- Website
- trackmcp.com
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