## Documentation Index

Fetch the complete documentation index at: [/llms.txt](/content/llms.txt)

Use this file to discover all available pages before exploring further.

04.18.2025

##  Tracing For MCP Client Server Applications

We’re excited to announce a powerful capability in the [**OpenInference**](https://github.com/Arize-ai/openinference) OSS library **`openinference-instrumentation-mcp` —** seamless OTEL context propagation for MCP clients and servers.

###  **What’s New?**

This release introduces automatic distributed tracing for **Anthropic’s Model Context Protocol (MCP)**. Using OpenTelemetry, you can now:

- **Propagate context** across MCP client-server boundaries
- Generate **end-to-end traces** of your AI system across services and languages
- Gain full visibility into how models access and use external context

The `openinference-instrumentation-mcp` package handles this for you by:

- Creating spans for MCP client operations
- Injecting trace context into MCP requests
- Extracting and continuing the trace context on the server
- Associating the context with OTEL spans on the server side

###  **Set up**

1. Instrument both MCP client and server with OpenTelemetry.
2. Add the `openinference-instrumentation-mcp` package.
3. Spans will propagate across services, appearing as a **single connected trace** in Phoenix.

[**phoenix/tutorials/mcp/tracing_between_mcp_client_and_server at main · Arize-ai/phoenix**](https://github.com/Arize-ai/phoenix/tree/main/tutorials/mcp/tracing_between_mcp_client_and_server)

###  **Walkthrough Video**

Tracing MCP Clients & Servers: How-To - YouTube

[Tracing MCP Clients & Servers: How-To](https://www.youtube.com/watch?v=dhK2pWQdGjk)

###  **Acknowledgments**

Big thanks to Adrian Cole and Anuraag Agrawal for their contributions to this feature.