Single tool 'execute_skill_action' has a generic, vague name that violates verb_noun conventions. Description is 127 chars but lacks WHAT/WHEN/WHY context required by LLMs. Input schema exists but parameter 'query_payload' is a free-form string with no format constraints, validation rules, or examples of valid payloads. No output schema documented, LLMs cannot predict response structure. No error handling guidance. Tool appears to be a catch-all wrapper rather than a focused, composable action. No security considerations (credentials, permissions, audit trail) addressed.

• Rename tool to a specific action: 'detect_review_authenticity', 'verify_ftc_compliance', or 'analyze_review_sentiment', one tool per concern. Current name conflates multiple responsibilities.
• Replace 'query_payload' string parameter with explicit, typed parameters: review_text (string), verified_purchaser (boolean), review_source (enum: amazon|bazaarvoice|custom). Document constraints and valid values.
• Document complete output schema with all fields: audit_id, authenticity_score (0 - 1 float), synthetic_ai_generated_flag (boolean), incentivized_review_flag (boolean), ftc_compliance_badge_required (boolean), mandatory_disclosure_tag (string), audit_trail_url (string).
• Expand description to 80 - 150 chars explaining: 'Analyzes review text to detect AI-generated content, incentivized reviews, and FTC compliance violations. Returns authenticity score and required disclosure tags.' Include when to call it.
• Add error handling: document what happens if review_text is empty, too long, or in unsupported language. Provide actionable error messages (e.g., 'Review text must be 10 - 5000 characters; got 3 chars').