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@@ -46,6 +46,7 @@ Use MCPcat for:
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-**User session replay** 🎬. Follow alongside your users to understand why they're using your MCP servers, what functionality you're missing, and what clients they're coming from.
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-**Trace debugging** 🔍. See where your users are getting stuck, track and find when LLMs get confused by your API, and debug sessions across all deployments of your MCP server.
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-**Existing platform support** 📊. Get logging and tracing out of the box for your existing observability platforms (OpenTelemetry, Datadog, Sentry) — eliminating the tedious work of implementing telemetry yourself.
MCPcat seamlessly integrates with your existing observability stack, providing automatic logging and tracing without the tedious setup typically required. Export telemetry data to multiple platforms simultaneously:
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```python
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from mcpcat import MCPCatOptions, ExporterConfig
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mcpcat.track(
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server,
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"proj_0000000", # Or None since MCPcat is optional when sending data to existing observability platforms
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MCPCatOptions(
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exporters={
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# OpenTelemetry - works with Jaeger, Tempo, New Relic, etc.
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"otlp": ExporterConfig(
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type="otlp",
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endpoint="http://localhost:4318/v1/traces"
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),
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# Datadog
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"datadog": ExporterConfig(
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type="datadog",
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api_key=os.getenv("DD_API_KEY"),
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site="datadoghq.com",
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service="my-mcp-server"
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),
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# Sentry
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"sentry": ExporterConfig(
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type="sentry",
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dsn=os.getenv("SENTRY_DSN"),
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environment="production"
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)
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}
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)
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)
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```
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Learn more about our free and open source [telemetry integrations](https://docs.mcpcat.io/telemetry/integrations).
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## Free for open source
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MCPcat is free for qualified open source projects. We believe in supporting the ecosystem that makes MCP possible. If you maintain an open source MCP server, you can access our full analytics platform at no cost.
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