Server has 15 well-named tools with consistent verb_noun patterns (get_, list_, create_, delete_, etc.). All tools have descriptions (10 - 200 chars, within baseline 34 - 392 range). However, input schemas are INFERRED from Python function signatures, not explicitly visible in JSON Schema format. Parameter descriptions exist in docstrings but are not formally declared in schema objects. Output schemas are completely undocumented, LLMs cannot predict return structure. Error handling is absent; no recovery guidance or categorization. Security considerations (force flags, backup options) are present but not formally gated. Composition is strong: tools are single-responsibility and chainable (e.g., list_profiles → get_profile → explain_key). Naming is excellent (18 - 24 chars, action-first). This is a solid domain-specific server hampered by schema formality and error handling gaps.

• Declare explicit JSON Schema for all tool inputs. Use 'enum' for mode, scope, severity, and ref parameters. Add 'minimum', 'maximum' for limit and numeric params. Example: {"mode": {"type": "string", "enum": ["raw", "resolved", "traced"], "description": "..."}}
• Document output schemas for every tool. Specify return object structure, field types, and nesting. Example: get_profile returns {"profile": {"name": string, "values": object, "source": string}, "inherited_from": string}.
• Add error handling with recovery guidance. Catch exceptions and return structured errors: {"error": "profile_not_found", "message": "Profile 'X' not found. Try list_profiles(type='machine') to see available profiles.", "retryable": false}.
• Implement dry-run or confirmation for destructive tools. Add a 'dry_run' parameter to delete_profile and set_values. Return a preview of changes and require explicit confirmation before applying.
• Add pagination support to list_profiles. Return {"profiles": [...], "total": N, "limit": 200, "offset": 0} and accept offset/limit params to handle large result sets without context explosion.