Single tool with adequate naming (run_sqlite_query) and clear description (194 chars, within baseline 34-392). Input schema present with type and description. Output schema documented in description. However, no parameter validation constraints (enum, min/max), no error recovery guidance, and security validation is basic (string prefix check only). Resources (schema://tables, schema://{table_name}) are well-documented but tool-focused evaluation shows moderate gaps in LLM-optimization patterns.

• Replace string prefix validation with parameterized queries or prepared statements. Use sqlite3.execute(query, params) to prevent SQL injection entirely.
• Add explicit error categorization: return {fail: true, error_type: 'syntax_error'|'timeout'|'permission_denied', error_message: '...', recovery_hint: 'Try...'} to guide LLM recovery.
• Document result limits in tool description: 'Returns up to 1000 rows. For larger datasets, use LIMIT and OFFSET pagination.' Add limit/offset parameters if needed.
• Add schema constraints: maxLength for query param (e.g., 10000 chars), document timeout (e.g., 30s), and list forbidden keywords beyond INSERT/UPDATE/DELETE (e.g., PRAGMA, ATTACH).
• Implement audit logging: log caller identity, query hash, execution time, and result row count to stderr or external audit service for compliance.