Critical gaps across all dimensions. Only 2 tools present with minimal schema documentation. Tool descriptions are present but extremely brief (18-31 chars, well below 50-200 char baseline for LLM optimization). Input schemas lack parameter descriptions entirely, the 'prompt' parameter in optimize_model has a description, but get_cost_summary has an empty input object with no documentation. No output schemas documented. No error handling guidance. No parameter constraints (enums, ranges, patterns). Tool names lack clarity on when to use each vs the other. No evidence of idempotent operation design or composition patterns.

• Expand tool descriptions to 80-150 characters. Example: 'optimize_model: Analyzes task complexity and recommends the most cost-effective LLM model. Call this before invoking expensive models to reduce costs.'
• Document output schemas for both tools. Specify what fields are returned (e.g., model_name, estimated_cost, reasoning for optimize_model).
• Add parameter descriptions to get_cost_summary. If it accepts filters (date range, model type, etc.), document each as a separate parameter with type and constraints.
• Add error handling guidance: 'If task complexity cannot be determined, returns error with available model options. Retry with more detailed prompt.'
• Consider splitting tools by responsibility: optimize_model selects a model; get_cost_summary reports historical costs. Ensure output from one chains into the other (e.g., return model_id that cost_summary accepts as filter).