Four tools with complete schemas and descriptions, but naming conventions deviate from English verb_noun patterns (Portuguese names like 'listar_tecnologias', 'buscar_trilha'). All tools have input schemas with type definitions and descriptions. Tool descriptions are adequate (100-150 chars) but lack actionable context for LLM selection. Error handling is present but minimal, no recovery guidance. Tool annotations (readOnlyHint, destructiveHint, idempotentHint) are correctly applied. Output structure is text-only; no structured response schemas documented.

• Rename tools to English verb_noun convention: 'listar_tecnologias' → 'list_technologies', 'buscar_trilha' → 'get_learning_path', 'gerar_desafio' → 'generate_challenge', 'gerar_certificado' → 'generate_certificate'. This aligns with LLM training and the 90% baseline of A+ tools.
• Enhance tool descriptions with WHEN/WHY context. E.g., 'get_learning_path: Returns the complete study plan (modules, badges, XP) for a technology. Call this after list_technologies to explore a specific learning path. Returns structured data suitable for progress tracking.'
• Document output schema formally. Define the structure of the text response (e.g., 'Returns a formatted string with sections: [Technology Name], [Level], [Modules], [Total XP]'). Consider returning structured JSON instead of plain text for better LLM parsing.
• Improve error messages with recovery guidance. E.g., 'Technology "xyz" not found. Available technologies: [list]. Call list_technologies to see all options.' This matches the recovery-guide pattern.
• Normalize the 'nivel' enum to a single canonical form (e.g., 'basico', 'intermediario', 'avancado') and handle case-insensitivity server-side. Remove redundant enum variants.