Four tools with clear naming (verb_noun pattern) and solid descriptions (150-250 chars, well above 20-char minimum). All tools have complete input schemas with typed properties and descriptions. However, output schemas are entirely undocumented, LLMs cannot predict return structure. No error handling guidance, no parameter constraints (enums), no pagination support despite compare_models likely returning large result sets. Missing idempotency hints and recovery guidance. Descriptions are good but lack 'when to use' context and dependency hints between tools.

• Document output schemas for all tools. Example for get_spots: 'Returns array of {model_id: string, ticker: string, provider: string, beta_sync: number, beta_batch: number|null, context_window: number}'. This lets LLMs plan downstream calls.
• Add enum constraint to provider parameter: inputSchema.properties.provider.enum = ['anthropic', 'openai', 'google', 'xai', 'mistral', 'deepseek']. Prevents hallucinated provider names.
• Add pagination to compare_models: accept limit (default 20, max 100) and offset parameters. Return total_count and next_offset in response. Cap results at 50 models per call.
• Enhance error handling: catch API errors and return actionable messages. E.g., 'Model "gpt-5" not found. Available models: gpt-4.1, gpt-4-turbo. Call get_spots() to list all.' This enables LLM self-correction.
• Add composition hints to descriptions. E.g., get_spots description: 'Use this first to discover available models. Then call get_greeks(model_id) for pricing structure or price_task() to estimate costs for your workload.'