Single tool with minimal definition quality. Tool name 'execute_skill_action' is generic and violates verb_noun clarity (pattern:tool). Description is vague (79 chars, below 100-char minimum for context) and lacks WHAT/WHEN/WHY guidance. Input schema exists but parameter 'query_payload' has only a 47-char description, insufficient for LLM to understand expected format, constraints, or examples. No output schema documented; response is hardcoded JSON string without field definitions. No error handling, no parameter validation, no recovery guidance. Tool appears to be a stub that always returns success regardless of input.

• Rename tool to 'map_scientific_consensus' or 'analyze_research_consensus' to clearly signal the action and domain. Follow verb_noun pattern (pattern:tool).
• Expand tool description to 100-200 chars: 'Analyzes scientific literature to determine consensus ratio on a research question. Returns affirmative/skeptical/inconclusive percentages and consensus grade. Call this to evaluate the strength of scientific evidence on a topic.'
• Document 'query_payload' parameter: 'Research question as a plain-text string (e.g., "Does intermittent fasting improve insulin sensitivity?"). Max 500 chars. Tool will analyze peer-reviewed studies and return consensus metrics.'
• Document output schema with typed fields: consensus_dossier_id (string), research_question (string), studies_analyzed (integer), affirmative_ratio (number 0-1), skeptical_ratio (number 0-1), inconclusive_ratio (number 0-1), consensus_grade (enum: STRONG_SCIENTIFIC_CONSENSUS|MODERATE_CONSENSUS|WEAK_CONSENSUS|INCONCLUSIVE), combined_sample_size (integer), consensus_breakdown_url (string URI).
• Add error handling: validate query_payload is non-empty and under 500 chars; return actionable errors like 'Query too short (min 10 chars)' or 'No studies found for this topic, try a more specific research question.'