MCP provides logging options primarily through the server side, where developers can instrument tool calls, validation steps, and error states. The protocol itself does not mandate a specific logging format, but it provides clear points in the communication flow where logs can be captured—such as during tool registration, tool invocation, input validation, backend execution, and response creation. Because messages follow predictable JSON structures, developers can log both requests and responses in a standardized way without parsing arbitrary data.
The MCP server can also log metadata exchange, client connection events, and error responses. Developers often create middleware layers that wrap each tool execution to record timestamps, argument structures, execution time, and any exceptions. For example, when a model makes a vector search request, the server can log the embedding size, the top-k value, and Milvus query latency. These logs help diagnose performance bottlenecks or detect malformed requests early in the pipeline.
For vector workflows, detailed logging is especially important because embedding mismatches, incorrect dimensions, or search anomalies can lead to inconsistent retrieval behavior. MCP-based tools make it easy to log vector dimensions, search scoring details, and key metadata fields without exposing sensitive data. Since the model always uses the same tool invocation pattern, logging remains consistent across all runs, enabling better observability and more reliable debugging of retrieval-augmented applications built on Milvus.