Teaching AI How to Refinery
Blog post from Honeycomb
In February, Refinery released version 3.1 of its tail-based sampling solution, offering performance enhancements, bug fixes, and new telemetry features. Accompanying this release was a new tool for the MCP server, designed to help AI systems understand Refinery and Honeycomb's sampling processes. The tool allows users to query AI assistants about their sampling rules, configuration, and sample rates. During testing, the AI assistant identified a critical issue in the sampling rules, specifically a misconfiguration in the end-to-end (E2E) data rule due to an unquoted integer value, which led to incorrect sampling rates. This discovery underscored the AI's capability in pattern-matching and problem-solving when integrated with Honeycomb's MCP server. The experience highlighted the importance of understanding rule configurations and the potential for AI tools to improve data management practices by identifying and diagnosing issues that might otherwise go unnoticed.