Five tools with basic schemas and descriptions, but significant gaps in parameter documentation and error handling. Tool names are descriptive (verify_json_schema, detect_hallucination_markers) but descriptions are brief (20-60 chars). Input schemas present but lack depth: parameters have types and basic descriptions, but no enums, constraints, ranges, or validation guidance. No output schemas documented. Error handling absent, tools do not guide recovery or categorize failures. No security considerations visible (e.g., how are LLM outputs validated before schema checking?). Composition is reasonable (each tool has one responsibility), but parameter relationships undocumented (e.g., sensitivity_level enum values not specified).

• Add output schemas to all tools. Document return types, field names, and structure. Example: verify_json_schema should return {valid: boolean, errors: array, schema_version: string}.
• Convert free-form string parameters to enums. Replace 'sensitivity_level' description with enum: ['low', 'medium', 'high']. Replace 'content_type' with enum: ['factual_response', 'analysis', 'summary'].
• Expand tool descriptions to 100-200 chars. Include WHEN to use (e.g., 'Call after LLM generates output to detect hallucinations before passing to next agent'), prerequisites, and side effects.
• Add error handling guidance. E.g., verify_json_schema should document: 'Returns {valid: false, errors: [...]} if schema validation fails. Agent should ask user to reformat data or try a different schema.'
• Add numeric constraints. E.g., max_age_hours: 'Integer, 1-8760 (1 year). Defaults to 24.' Prevents LLMs from passing absurd values like 999999.