Server has well-structured tool definitions with proper Pydantic schemas and clear naming conventions. All 5 tools start with action verbs (github_skills_*). Descriptions are present and substantive (avg ~120 chars). Input schemas are complete with type constraints, min/max bounds, and enums. However, output schemas are not documented, responses are described only in prose. Parameter descriptions are good but some lack actionable format guidance. No tool annotations (readOnlyHint/destructiveHint) despite clear risk profiles. Error handling is minimal, no recovery guidance or categorization.

• Document output schemas for all tools. Specify: for search_skills, return array of {skill_slug, skill_name, repo_name, score, matched_terms}; for get_skill, return {skill_slug, content, files: [{name, body}]}. Include field types and whether fields are always present.
• Add tool annotations: mark github_skills_install_skill with destructiveHint=true (modifies filesystem); mark all read-only tools with readOnlyHint=true. Use MCP tool annotation syntax in tool registration.
• Implement structured error responses. Catch GitHub API errors and return {error: 'skill_not_found', message: 'Skill public-skills:unknown not found. Try search_skills(query=...) to discover available skills.', recovery_hint: 'search_skills'}.
• Formalize skill_slug format in parameter descriptions: 'Skill identifier in format repo-name:skill-name (e.g., public-skills:figma-implement-design). Obtain from search_skills() results.'
• Add pagination guidance to search_skills: document that results are sorted by relevance score; if more results exist beyond limit, suggest re-querying with refined query or higher limit (max 50).