Three tools with clear verb-noun naming (query_, log_, verify_) and documented descriptions. However, parameter descriptions are minimal or absent, output schemas are not formally documented, and error handling lacks recovery guidance. query_failures returns structured data but log_failure and verify_resolution lack detail on success/failure cases. No input validation constraints (enums, ranges) visible. Descriptions are adequate (50-150 chars) but parameters lack the depth needed for LLM reasoning.

• Document output schemas formally. For query_failures, specify: returns list of {id: string, error: string, solution: string, rule: string|null, verified: int, score: float}. For log_failure: {id: string, rule: string}. For verify_resolution: {verified: boolean, message?: string}.
• Add enum constraint to 'scope' parameter in log_failure: scope must be one of ['local', 'public']. Document when to use each (local = team-only, public = shared across teams).
• Add range constraints to numeric parameters: limit in query_failures should be 1 - 100 (default 5). Document the rationale (e.g., 'Limit results to prevent context window exhaustion').
• Enhance error handling with recovery hints. E.g., if failure_id not found, return: 'Failure ID not found. Call query_failures() to find similar failures first.' If embedding fails, return: 'Search failed due to embedding service. Retry in 30s or check NEVER_AGAIN_EMBEDDER config.'
• Clarify log_failure 'rule' parameter: 'Optional. If provided, use this WHEN/CHECK/BECAUSE rule. If omitted, the engine generates one from error/solution/context. Provide a rule when you have specific prevention logic in mind.'