Server has 6 well-named tools with complete input schemas and descriptions. Naming follows verb_noun convention (calculate_cost, estimate_monthly, compare_models, find_cheapest, estimate_tokens, list_models). All tools have 10-200 char descriptions and required parameters are clearly marked. However, output schemas are not documented, LLMs cannot predict response structure for downstream chaining. Parameter descriptions are minimal (avg 40 chars vs baseline 72). No error handling guidance, no recovery hints, and no per-item success/failure patterns for batch operations. Missing tool annotations (readOnlyHint, idempotentHint). All tools are read-only, which is good for safety but should be explicitly declared.

• Add output schema documentation to each tool. E.g., calculate_cost returns {cost: number, model: string, input_tokens: number, output_tokens: number, batch: boolean}. Include field types and descriptions.
• Enhance parameter descriptions with constraints and ranges. E.g., 'requests_per_day: Average requests per day (1-10000, integer)' and 'batch: Use batch pricing if available (reduces cost by ~50% for Anthropic/OpenAI).'
• Add readOnlyHint: true to all tool schemas to signal safety. Update schema registration: {name: 'calculate_cost', ..., inputSchema: {...}, readOnlyHint: true}.
• Implement error recovery guidance. E.g., if model not found, return {error: 'Model "gpt-5" not found. Available models: gpt-4o, gpt-4-turbo, claude-3-opus. Call list_models() to see all.'}
• Standardize list_models response. Always return {models: [...], total: number, pricing_version: string} regardless of provider filter. Remove conditional response structure.