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Scored across 10 tools
Each tool targets a distinct stage or artifact (audit, draft examples, create/validate config, check output, rank results, generate routing, cost/memory estimates, sensitive-data scan). The only mild overlap is between cost-related tools (rank_eval_results vs estimate_cost) and config-checking (validate_eval_config vs check_output), but the descriptions clearly separate config validation from single-reply checking.
All ten tools share the modelfinder_ prefix and use a consistent snake_case verb_noun pattern (audit_models, draft_eval_examples, create_eval_config, rank_eval_results, etc.). No convention mixing or vague names.
Ten tools is well within the ideal range and each maps to a concrete step in the model-eval-and-routing workflow, so none feel redundant or padded.
The surface covers the full planning pipeline: auditing model usage, drafting examples, building/validating configs, checking outputs, ranking results, generating routing, and estimating cost/memory, plus PII scanning. The only gap is that actual eval execution is delegated externally (config is saved for eval_models.py), leaving a slight end-to-end dead end.