TrackMCP
TrackMCP is an analytics and observability service for MCP (Model Context Protocol) servers that captures every tool call, synthesizes sessions and outcomes, and surfaces actionable insights via a dashboard and SDKs.
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Quick Overview
Best for: Business Intelligence
What it does
AI Tools software for decision-makers comparing workflow fit and alternatives.
Best fit
Business Intelligence
Pricing snapshot
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TrackMCP
TrackMCP provides analytics and observability specifically for MCP (Model Context Protocol) servers. By adding a single line of integration to an existing MCP server, TrackMCP captures every tool call, groups them into sessions and outcomes, attributes activity to AI clients, detects failures and silent errors, and surfaces plain-English explanations and suggested fixes in a dashboard. It is aimed at teams and developers running MCP servers who need to understand usage patterns, reliability issues, and where workflows are failing so they can prioritize fixes and improvements.
Google Analytics for MCP Servers Discussion | Link
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Claim this listing for $29Key Features
One-line integration
Add one line to your MCP server (withTrackMCP) to start sending events to TrackMCP without manual event tagging.
Official SDKs
Provides official TypeScript and Python SDKs; installation example shown: npm i @trackmcp/sdk.
Real-time dashboard
Data shows up in the dashboard in real time with overviews for clients, tools, sessions, outcomes, and reliability.
Session and outcome synthesis
Groups tool calls into sessions and outcomes, showing workflows, where sessions stop, and whether work completes.
Reliability and error analysis
Detects high-error flows (example: send_email failing 94%), summarizes retries and schema mismatches, and suggests fixes.
Client attribution
Identifies which AI clients connect (e.g., Claude, Cursor, ChatGPT, Custom agents), counts active and returning clients, and tracks client adoption.
Actionable English summaries
Converts usage data into short plain-English explanations and suggested next steps (e.g., accept string as well as array).
Pricing
Current pricing details are not available from the vendor source.
Use Cases
Observability for MCP servers
Monitor who is using your MCP server, which tools they call, and whether workflows complete successfully.
Reliability debugging
Identify failing tool calls, retries, schema mismatches, and get suggested fixes to reduce silent failures and improve completion rates.
Product and adoption analytics
Measure client adoption, returning usage, and which workflows or tools are most used to prioritize product improvements.
Operational alerting and triage
Surface items that need attention (e.g., tools with high failure rates) so teams can focus debugging efforts on the highest impact issues.
Integrations
TypeScript SDK
Official TypeScript SDK for integrating TrackMCP with Node-based MCP servers (shown via npm install).
Python SDK
Official Python SDK for integrating TrackMCP with Python-based MCP servers.
MCP clients (Claude, Cursor, ChatGPT, Custom agents)
Works with every MCP client and attributes calls to specific AI clients.
Server logs & APM/tracing
Designed to complement server logs and APM/tracing by adding an analytics layer that explains who is using the server and where workflows fail.
Benefits
Limitations
No verified limitations are available.
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Install the SDK: run npm i @trackmcp/sdk (TypeScript) or use the Python SDK as documented.
- 2 Wrap your existing MCP server with the SDK (example: export default withTrackMCP(server, { apiKey: process.env.TRACKMCP_KEY, service: "your-service" })).
- 3 Deploy the updated server; TrackMCP will capture calls and display data in the dashboard in real time.
- 4 Open the TrackMCP dashboard to review clients, tools, sessions, outcomes, and suggested fixes.
Support
docs
Documentation and API reference available from the TrackMCP site (Docs, TypeScript SDK, Python SDK, API reference).
blog
Product updates and articles available via the TrackMCP blog linked from the site.
contact
Contact via the TrackMCP website contact link listed in the site navigation/footer.
API
API reference available on the TrackMCP documentation pages (linked from the site).
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