Towards Agent-Friendly Documentation for Database Tuning

System-tuning documentation provides parameter recommendations but often leaves their deployment-specific validation implicit, limiting their relevance across environments. We present PerfEvolve, a framework that augments static tuning guidance with executable procedural knowledge for LLM-based tuning agents. Offline, PerfEvolve ranks parameters by sensitivity, screens candidate ranges, and identifies interacting groups, compiling the results into procedures with measurements, decision rules, and validation checks. At deployment, these procedures guide local experiments or supply empirical priors to existing tuners. On PostgreSQL 16 with TPC-C and TPC-H workloads, integrating PerfEvolve into GPTuner improves matched-environment performance by 1.4%–6.1%, increases the valid-trial rate from 68%–81% to 100%, and yields gains of 45.8%–58.9% under cross-hardware transfer. With E2ETune, the full procedural representation achieves a 35.2% improvement over default PostgreSQL. These results support combining executable procedures with empirical reference data to improve tuning reliability and adaptation across deployments. Code and artifacts are available at https://anonymous.4open.science/r/PerfEvolve-DF10.