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Scored across 28 tools
Most tools are clearly separated by domain and by the detail-vs-score pattern (e.g., security_scan vs. get_security_score). The main ambiguity is between analyze_imports and analyze_coupling, which both report instability and hub-like module metrics, so an agent could pick the wrong one for architecture analysis.
The server uses a consistent snake_case verb_noun convention, and the get_*_score tools form a recognizable summary pattern. However, verb choice varies across analyze/find/audit/scan/check/detect, and get_import_health and full_health_check break the get_*_score pattern, creating minor inconsistency.
At 28 tools, the server exceeds the comfortable range and will likely burden agent selection. The high count is understandable given the detail-plus-score pairing across many health dimensions, but it still feels oversized for a single MCP server.
The suite covers an impressively broad set of Python code-health domains: complexity, security, imports, clones, tests, types, naming, todos, bugs, docstrings, dependencies, hotspots, env vars, and git history. Minor gaps include no actual test coverage percentage and no aggregate score for dead code or clone analysis.