Strong tool naming (all verb_noun pattern), comprehensive parameter schemas with enums and constraints, and detailed descriptions. All 7 tools have clear, actionable descriptions (100-250 chars). Input schemas are well-structured with type definitions, ranges, and enum constraints. However, output schemas are not explicitly documented in the source code, responses are described narratively but lack formal JSON Schema definitions. Error handling is minimal; no recovery guidance or actionable error messages visible. The tesla_send_command tool has complex parameter validation (per-command params) that is documented in description but not enforced via schema constraints.

• Add formal JSON Schema definitions for all tool outputs. Document field names, types, and whether fields are always present or conditional. Example: tesla_get_drives should return {drives: [{id, start_time, end_time, distance_km, distance_mi, ...}], total_count, has_more}.
• Refactor tesla_send_command 'params' schema to use oneOf with discriminator on 'command' field. Each command variant should declare its own required/optional params with type and range constraints. This enables schema-level validation and LLM self-correction.
• Add error handling documentation to each tool. Specify: (1) common failure modes (auth, rate limit, vehicle asleep), (2) whether retryable, (3) recovery action. Example: 'If vehicle is asleep and command has wake_seconds=0, error is retryable with wake_seconds > 0.'
• Implement a dry-run or confirmation pattern for tesla_send_command. Add optional 'dry_run' parameter (default false) that returns what would be sent without executing. For destructive commands, require explicit confirmation via a separate confirm_command tool.
• Document pagination fully: add total_count and has_more fields to output schema for tesla_get_drives, tesla_get_charges, tesla_get_trips_for_month. Clarify in descriptions: 'Returns up to 50 results; use offset to fetch next page.'