Three tools with clear, domain-specific purposes (persistent memory chain). Tool names are action-oriented (remember, recall, chain_status). Descriptions are present and contextual (50-150 chars each), explaining WHEN to use each tool. Input schemas are visible with type definitions. However, parameter descriptions lack depth, 'content' and 'query' are minimally explained. Output schemas are not formally documented; responses are free-text strings rather than structured objects. Error handling is minimal (e.g., 'Erro: conteúdo vazio' for empty content, but no guidance on recovery). No input validation constraints (enums, ranges, patterns) visible. The server is functionally sound but lacks the rigor expected of production-grade tools.

• Convert all tool responses from free-text strings to structured JSON objects. E.g., remember() should return {seq: int, hash: str, chain_length: int}; recall() should return {count: int, memories: [{seq, ts, content, tags}]}; chain_status() should return {is_valid: bool, chain_length: int, first_ts: float, last_ts: float, errors: []}.
• Add explicit constraints to parameters: 'limit' (1 - 100, default 10), 'tags' (array of 0 - 10 strings, each 1 - 50 chars), 'content' (1 - 10000 chars, UTF-8). Document these in parameter descriptions.
• Enhance error messages with recovery guidance. E.g., 'Content is empty. Provide a non-empty string to persist a memory.' or 'No memories found with query="xyz". Try recall() with a shorter query or different tag.'
• Add pagination support to recall(). Return {count: int, total: int, memories: [...], next_cursor: str|null} to handle large result sets without blowing context.
• Document the output schema for each tool in the docstring. E.g., 'Returns: {seq: int, hash: str, chain_length: int}' so LLMs know what fields to expect.