33 tools with consistent naming (verb_noun pattern) and basic descriptions. However, critical gaps: NO input schemas visible in source code for ANY tool, all schema scores are 0. Descriptions average ~80 chars, below the 194-char baseline for A+ tools. Parameter descriptions exist in the input schema map but are minimal (e.g., 'Widget name', 'Post ID'). No output schemas documented. Error handling is absent, no recovery guidance, no error classification. Security: no evidence of permission checks, audit logging, or secret injection patterns. Composition is sound (single responsibility per tool), but lack of schema documentation and error guidance severely limits LLM usability.

• Expose the input_schema() method implementation in source code. Verify all 33 tools have JSON Schema with type, description, and constraints (enum, minLength, pattern, minimum, maximum) for every parameter.
• Document output schemas for all tools. For list_* tools, include pagination (limit, offset, total_count). For create_* and revise_* tools, return the created/updated object with all IDs needed for downstream calls.
• Add error handling: categorize errors as retryable (network timeout, rate limit) vs. user-fixable (invalid post_id, permission denied) vs. fatal. Return actionable messages: 'Post ID 999 not found. Call list_ai_drafts() to see available drafts.'
• Expand parameter descriptions to 50 - 150 chars. Include format, range, and valid values. E.g., 'Page number (1 - 1000, default 1)' instead of 'Page number'. Use enums for constrained fields like engine ('atomic' or 'legacy').
• Implement permission checks and audit logging. Gate destructive tools (delete_ai_draft, rollback_action) behind permission verification. Log all tool calls with caller ID, timestamp, parameters, and result for compliance.