Atlas exposes 3 well-intentioned tools with solid descriptions (194 - 250 chars, above baseline 194 avg) and clear safety models. All tools are read-only and parameterized. However, input schemas lack explicit type declarations and constraints in the visible code. Tool names follow verb_noun convention (run_*, detect_*, describe_*). Descriptions explain WHAT and WHEN but lack actionable error recovery guidance. No output schema documentation visible. Parameter descriptions are present but sparse on constraints (e.g., z_threshold lacks range guidance). Error handling returns dicts but no recovery hints. Composition is sound, tools chain logically (describe_dataset → run_bigquery). Security model is strong (parameterized queries, no secrets in params). Overall: above-average descriptions and naming, but schema documentation and error guidance are gaps.

• Document output schemas for all three tools. E.g., run_bigquery returns {status: 'success'|'error', rows: [{col1: val, ...}], row_count: int, query_cost_bytes: int}. describe_dataset returns {tables: [{name, description, columns: [{name, type, description}]}]}.
• Add explicit JSON Schema constraints: z_threshold {type: 'number', minimum: 0.5, maximum: 5.0}, lookback_days {type: 'integer', minimum: 1, maximum: 365}, sql {type: 'string', pattern: '^(SELECT|WITH)\\s', description: '...'}.
• Enhance error responses with recovery hints. E.g., if query times out, return {status: 'error', code: 'TIMEOUT', message: 'Query exceeded 30s. Try adding LIMIT 1000 or narrowing the date range.', retryable: true}.
• Add parameter descriptions for describe_dataset (currently empty input). If it accepts optional filters (e.g., table_name_pattern), document them.
• Document pagination/limits for run_bigquery results. E.g., 'Results capped at 10,000 rows; use LIMIT and OFFSET in SQL for large datasets.'