The Model Context Protocol (MCP) is crucial for connecting AI agents to external tools and data, and understanding the behavior of MCP clients, such as agents, gateways, and IDEs, is vital for efficient system operation. Datadog's LLM Observability now offers comprehensive tracing and monitoring for these clients, capturing every step from session initialization to tool invocation as part of a span linked to the LLM trace that initiated the tool selection. This enhanced visibility helps teams trace client-side registry discovery, tool invocation behavior, and their contribution to latency and token usage, thereby pinpointing failures and measuring efficiency. By automatically instrumenting the MCP Python client library, teams gain insights into the complete MCP life cycle, enabling them to identify slow or unreliable MCP servers, track connection latency, and correlate failures with specific tools or prompts. Additionally, the observability platform aggregates MCP span data to provide key performance metrics, such as latency and error rates, allowing engineers to improve registry configurations and enhance AI agent performance. This level of detail supports quicker issue resolution, reduced unnecessary tool invocations, and optimized MCP integrations in production environments.