This server exhibits critical gaps across naming, descriptions, and schema definition. While 6 tools are exposed via Spring AI's ToolCallbacks mechanism, the source code does not reveal explicit schema registration, parameter type definitions, or detailed descriptions. Tool names lack consistent verb prefixes and descriptions are minimal (8-65 chars, well below the 50-200 char optimum). Input schemas are inferred from the parameter audit provided but not visible in the source code itself, this triggers hard scoring caps. No error handling, validation guidance, or recovery paths are documented. No parameter descriptions beyond what the audit summary provides. The server appears functional for a demo use case but falls far short of production tool standards.

• Rename tools with English action verbs: 'list_students' instead of 'todosDiscentes', 'list_students_by_city' instead of 'discentesPorCidade', 'create_student' instead of 'criarDiscente', 'delete_student' instead of 'excluirDiscente'.
• Expand all descriptions to 80 - 150 characters. Example: 'todosDiscentes' → 'Retrieves a complete list of all enrolled students. Use this to get an overview of the student body, then filter by city, course, or research line as needed.'
• Add explicit parameter descriptions in the tool definition (not just in audit summaries). For 'cidade', specify: 'The city name (exact match, case-insensitive). Examples: São Paulo, Rio de Janeiro.'
• Document the output schema for each tool. For 'todosDiscentes', describe: 'Returns an array of Student objects, each containing: id (string), nome (string), cidade (string), curso (string), linhaPesquisa (string).'
• For destructive operations like 'delete_student', add a 'confirm' boolean parameter and require explicit confirmation before execution, or implement a dry-run mode.
• Add error recovery guidance in descriptions. E.g. 'If a city is not found, an empty list is returned. Try list_students to see available cities.'
• Define enum constraints for 'curso' and 'linhaPesquisa' if they have a fixed set of valid values (e.g., cursos: ['Mestrado', 'Doutorado'], linhas: ['IA', 'ML', 'NLP']). This prevents LLMs from inventing invalid values.
• Expose the actual schema definitions in the Spring AI @Tool or @ToolParam annotations so they are visible in the source code and can be verified at review time.