Single tool 'execute_skill_action' has a minimal description (97 chars) that lacks actionable context. Input schema is present but parameter 'query_payload' has only a generic description ('Input execution payload for this skill'). No output schema is documented, the tool returns a hardcoded JSON string with no structure definition. No error handling guidance, no parameter constraints, and the tool name uses a generic verb ('execute') rather than a domain-specific action. The implementation is a stub that always returns success, providing no real functionality to evaluate.

• Rename tool to a specific action verb: e.g., 'analyze_study_methodology', 'detect_research_bias', or 'audit_study_design', something that tells the LLM exactly what domain action it performs.
• Document the output schema explicitly. Define the response structure with typed fields: e.g., {type: 'object', properties: {bias_audit_id: {type: 'string'}, methodology_robustness_grade: {type: 'string', enum: ['TIER_1_RIGOROUS', ...]}, ...}, required: [...]}}.
• Rewrite the tool description to be actionable: 'Analyzes a research study design for methodological bias, p-hacking risk, and statistical power. Returns a bias audit report with a robustness grade (TIER_1_RIGOROUS, TIER_2_ACCEPTABLE, TIER_3_CONCERNING). Call this to validate study methodology before recommending or citing research.'
• Add parameter constraints and format guidance: 'query_payload (string): A JSON-encoded object with keys: study_id (string), study_design (enum: DOUBLE_BLIND_RCT, OPEN_LABEL_RCT, OBSERVATIONAL, ...), cohort_size (integer, 10-10000). Example: {"study_id": "study_rct_2026_09", "study_design": "DOUBLE_BLIND_RANDOMIZED_CONTROLLED_TRIAL", "cohort_size": 250}'.
• Add error handling guidance: 'If the skill fails, the response will include an error_code and error_message. Retryable errors (e.g., service_unavailable) can be retried after 5 seconds. Non-retryable errors (e.g., invalid_study_design) require the user to correct the input.'