No user-submitted related servers found.
Scored across 10 tools
Each tool has a distinct resource+action, and descriptions explicitly cross-reference neighbours (create vs validate eval config, check_output vs rank_eval_results, rank vs generate_routing), which makes selection reliable. Minor residual overlap: modelfinder_scan_sensitive_data duplicates screening already performed by modelfinder_create_eval_config, and both rank_eval_results and generate_routing consume the same results.json, so an agent may hesitate on which to call first.
All ten tools use the same modelfinder_ prefix followed by a clear verb_noun pattern (audit_models, create_eval_config, rank_eval_results, estimate_cost, etc.). No mixed casing or stylistic outliers; names are predictable and readable.
Ten tools is squarely in the well-scoped range and each maps to a discrete step in a coherent model-evaluation workflow (audit, draft, configure, validate, check, scan, rank, route, cost, memory). No tool appears redundant or filler.
The lifecycle is largely covered: discover models in a repo, draft examples, build and validate a config, check outputs, rank results, generate routing, and estimate cost/memory. Notable gaps remain—there is no tool to actually execute the eval run (delegated to an external eval_models.py) and, despite the 'Model Finder' name, no model catalog or pricing lookup, forcing users to supply prices manually—but these are documented and workable.