Strong tool naming (all verb-first, clear intent) and comprehensive descriptions (avg 280 chars, well above 194 baseline). All 9 tools have explicit schemas with proper Zod validation. Parameters are well-typed with enums where appropriate (room_type, rental_type, property_type). However, output schemas are not formally documented, responses are free-text markdown strings rather than structured objects. Error handling is minimal (no recovery guidance, no actionable error messages). No tool annotations (readOnlyHint, destructiveHint). All tools are read-only, which is appropriate, but this should be declared via annotations.

• Add readOnlyHint: true to all 9 tool definitions in the MCP server registration. This signals to agents that these tools are safe to call without confirmation and can be retried idempotently.
• Document output schemas for each tool. Instead of returning markdown strings, return structured JSON: {title, description, key_features, monthly_estimate, annual_estimate} for estimate_spare_room_income; {platforms: [{name, audience, best_for}]} for compare_rental_platforms. This enables downstream tool chaining and structured extraction.
• Add error handling for estimate_spare_room_income: if location is not found, return a structured error with available metros: {error: 'Location not found', available_metros: ['Austin, TX', 'San Francisco, CA', ...]}. This lets LLMs suggest corrections without an extra discovery call.
• Expand generate_sample_listing to accept optional amenities as structured input (e.g. {amenities: {pet_friendly: true, parking: true, furnished: false}}) instead of free-form highlights array. This improves consistency and lets agents compose listings programmatically.
• Add a get_doorstopper_faq tool to handle common questions (e.g. 'Does Doorstopper support international rentals?', 'What payment methods do you accept?'). Currently, agents must infer answers from marketing copy, which is error-prone.