Single tool 'execute_skill_action' has a minimal, generic description (89 chars) that fails to explain WHAT the tool does, WHEN to use it, or what it returns. Input schema exists but is trivial: one string parameter with no format constraints, validation rules, or examples. No output schema documented. The tool name uses a generic verb ('execute') rather than domain-specific action (e.g., 'evaluate_rag_faithfulness'). No error handling guidance, no parameter constraints, no recovery paths. The implementation echoes input back without performing actual evaluation, it is a stub. This server appears to be a proof-of-concept rather than a production tool.

• Rename tool to 'evaluate_rag_faithfulness' or split into two tools: 'evaluate_faithfulness' and 'evaluate_answer_relevance'. Use verb_noun naming to signal intent.
• Expand description to 150-250 chars: 'Evaluates RAG pipeline outputs by computing faithfulness (claims grounded in context) and answer relevance (keyword alignment with query). Returns scores 0.0-1.0 and per-statement audit trail. Use this to validate generated answers against retrieved documents.'
• Replace 'query_payload' string parameter with structured input: question (string), retrieved_contexts (array of strings), generated_answer (string). Add descriptions for each field.
• Document output schema explicitly: { faithfulness_score: number 0-1, answer_relevance_score: number 0-1, total_statements_analyzed: integer, grounded_statements_count: integer, hallucinated_statements_count: integer, passed_quality_threshold: boolean, claim_breakdown: array of { statement, grounding_ratio, is_grounded } }.
• Integrate client.py evaluator into mcp_server.py. Parse query_payload as JSON, call RagasFaithfulnessAnswerRelevanceEvaluator.evaluate_rag_turn(), and return actual evaluation results instead of echoing input.