Single tool 'execute_skill_action' has a vague, generic name that violates verb_noun conventions. Description (97 chars) is below the 194-char baseline and lacks specificity about what 'Deep Research & Scientific Consensus operations' means. Input schema is minimal: only one string parameter with no format constraints, enums, or validation guidance. No output schema is documented, the tool returns a hardcoded JSON string with no field descriptions. No error handling, recovery guidance, or examples. The implementation is a stub that ignores the input payload entirely, returning a static success message. This violates 8+ core patterns: tool naming, parameter description, output schema documentation, error handling, and composition.

• Rename tool to a specific action: 'score_citation_credibility', 'analyze_research_consensus', or 'verify_paper_citations', something that tells the LLM exactly what happens when called.
• Expand tool description to 150 - 250 chars explaining: (1) What it does (e.g., 'Analyzes citation patterns to score research credibility'), (2) When to use it (e.g., 'Call this to verify if a paper's claims are well-supported'), (3) What it returns (e.g., 'A credibility score 0 - 1 and citation breakdown').
• Add input schema constraints: define 'query_payload' as a structured object with required fields (e.g., paper_title, paper_doi, or paper_url), not a free-form string. Add enum or pattern constraints for valid inputs.
• Document the output schema explicitly in the tool definition. Include field names, types, and descriptions: e.g., 'status' (string: SUCCESS|ERROR), 'credibility_score' (number 0 - 1), 'supporting_citations' (integer), 'citation_badge' (string enum).
• Implement actual logic: parse the input payload, call the GenPark API or internal research service, and return real results. Remove the hardcoded stub response. Add error handling that returns actionable messages (e.g., 'Paper not found. Try searching by DOI or title.').