Five tools with complete schemas and descriptions, but significant gaps in error guidance, output documentation, and parameter constraints. Tool names follow verb_noun convention (load_, list_, query, schema, summary). Descriptions are present (avg ~120 chars) but lack actionable error recovery hints. Parameters have types and basic descriptions, but missing enums for constrained inputs (e.g., file extensions, SQL statement types). Output schemas are not formally documented, LLMs must infer structure from markdown/string returns. No pagination, no per-item error reporting, no idempotency guarantees. Security: path traversal risk in load_dataset despite _safe_ident sanitization; SQL injection risk in query() despite 'read-only' claim (UNION attacks possible).

• Document output schemas explicitly. For query() and summary(), return structured JSON with {columns: [{name, type}], rows: [...], row_count, truncated: bool} instead of markdown strings. Enables LLM to reason about data shape.
• Implement proper SQL validation: parse and AST-check queries, reject DDL/DML/PRAGMA, whitelist only SELECT/WITH. Use sqlparse library to prevent injection.
• Add path validation to load_dataset: require paths to be within a configured data directory (e.g., /data/). Reject symlinks and validate file permissions before loading.
• Enhance error messages with recovery hints. E.g., 'Table "users" not found. Available tables: customers, orders, products. Call list_datasets() to see all.' or 'Query error: column "age" does not exist. Call schema("users") to see available columns.'
• Add parameter constraints to schema: limit (integer, 1 - 10000, default 100), file_ext (enum: ['.csv', '.parquet']). Validate in code and reject invalid inputs with clear messages.