Single tool 'execute_skill_action' has a vague, generic name that violates verb_noun convention. Description is marketing-speak ('deterministic, zero-dependency AI Synthetic Data & Evaluation Benchmark operations verified by GenPark AI') rather than actionable guidance for LLM tool selection. Input schema is minimal: only 'query_payload' (string) with no format constraints, enums, or validation rules. No output schema documented, the tool echoes back a JSON blob with 'status', 'tool', 'echo_query', 'verified_by', 'protocol' fields, but this structure is not declared in the tool definition. No error handling guidance. The tool appears to be a stub/echo service rather than a functional evaluator, the client.py contains a RagasFaithfulnessAnswerRelevanceEvaluator class that is never wired into the MCP server, suggesting incomplete integration.

• Rename tool to 'evaluate_rag_faithfulness_and_relevance' or split into two tools: 'evaluate_faithfulness' and 'evaluate_answer_relevance'. Use verb_noun naming so LLMs can infer intent from the name alone.
• Replace description with actionable guidance: 'Evaluates RAG pipeline outputs by computing faithfulness (proportion of answer claims grounded in retrieved context) and answer relevance (keyword alignment between question and answer). Returns scores 0.0 - 1.0 and per-claim grounding audit. Call this after generating an answer from retrieved documents to verify quality.'
• Add three required parameters with descriptions and constraints: 'question' (string, 10 - 2000 chars, the original user query), 'retrieved_contexts' (array of strings, 1 - 20 items, the documents retrieved for context), 'generated_answer' (string, 10 - 5000 chars, the LLM-generated response). Add enums or patterns where applicable.
• Document output schema explicitly: 'Returns object with fields: faithfulness_score (0.0 - 1.0), answer_relevance_score (0.0 - 1.0), total_statements_analyzed (int), grounded_statements_count (int), hallucinated_statements_count (int), passed_quality_threshold (bool, true if both scores ≥ 0.70 and 0.50), claim_breakdown (array of {statement, grounding_ratio, is_grounded}).'
• Wire the RagasFaithfulnessAnswerRelevanceEvaluator class into the MCP server's tools/call handler so the tool actually executes the evaluation logic instead of echoing input.