Single tool 'execute_skill_action' has a minimal description (89 chars, below 194-char baseline) that lacks WHAT, WHEN, and actionable context. Input schema exists but parameter 'query_payload' has only a generic description ('Input execution payload') with no format, constraints, or examples. No output schema documented, the tool returns a hardcoded JSON string with no field descriptions. No error handling guidance, no idempotency guarantees, no security considerations. The tool name 'execute_skill_action' is vague (does not start with a clear verb like 'audit_', 'verify_', or 'check_') and does not reflect the stated domain (RAG faithfulness auditing). Tool definition is visible in code but implementation is a stub returning mock data, not a real auditor.

• Rename tool to 'audit_rag_faithfulness' or 'verify_rag_hallucination' to clearly signal the action and domain. Start with a verb that reflects auditing/verification.
• Expand tool description to 150 - 250 chars: 'Audits a RAG-generated answer against source context snippets to detect hallucinations, measure faithfulness (0 - 100%), and identify unsupported claims. Call this after generating an answer from retrieved documents to verify accuracy before returning to the user.'
• Document the input schema: 'query_payload' should be replaced with three explicit parameters: 'context_snippets' (array of strings, source documents), 'generated_answer' (string, the LLM-generated response), 'user_query' (string, the original user question). Add descriptions for each.
• Document the output schema with field descriptions: 'faithfulness_score_pct' (0 - 100, percentage of answer supported by context), 'hallucination_detected' (boolean), 'unsupported_claims_count' (integer), 'evaluation_audit_dossier_url' (string, link to detailed audit report).
• Add error handling: document what happens if context_snippets is empty, if generated_answer is null, or if the auditor service times out. Provide recovery guidance (e.g., 'If audit times out, retry with shorter context snippets or check service status at https://eval.rag.genpark.ai/status').