Five tools with complete input schemas and descriptions, but significant quality gaps. All tools use generic guide/info naming patterns rather than action verbs (get_, search_, list_). Descriptions are present but lack specificity on WHEN to use each tool vs. alternatives. Parameters have types and descriptions, but lack enums for constrained inputs (e.g., 'area_type' accepts free-form strings instead of enum ['suburb', 'estate']). Output schemas are not documented, LLMs cannot predict response structure. All tools marked READ_ONLY via annotations (good), but no error handling guidance. Data is explicitly marked DEMO, limiting production utility. Tool composition is reasonable (5 distinct housing domains), but naming conventions and output documentation are weak.

• Rename tools to action verbs: 'rental_market_guide' → 'get_rental_prices', 'tenant_rights_guide' → 'search_tenant_rights', 'building_permit_guide' → 'get_building_permit_process', 'affordable_housing_guide' → 'list_affordable_housing_programs', 'housing_finance_guide' → 'get_housing_finance_options'. This matches the verb_noun baseline (90% of A+ tools) and improves LLM intent inference.
• Document output schemas for all tools. Add a 'Returns' section to each tool docstring specifying field names, types, and structure. E.g., rental_market_guide returns {source: str, county: str, bedrooms: int, area_type: str, estimated_rent_kes: str, platforms: list[str], tip: str}. This enables LLMs to plan chaining and extract data reliably.
• Convert free-form string parameters to enums. Define area_type as enum ['suburb', 'estate']; topic as enum ['eviction', 'rent_increase', 'deposit', 'maintenance', 'utilities', 'lease', 'dispute']; income_range as enum ['under_15000', '15000_30000', '30000_50000', '50000_100000', 'above_100000']. Enums are self-documenting and prevent hallucinated values.
• Add error handling and recovery hints. Document what happens if county is not in RENTALS dict (currently returns default). Add guidance: 'If county not found, try a nearby county or call list_counties() first.' Return structured error responses with actionable next steps, not silent defaults.
• Enhance descriptions with WHEN-to-use context. E.g., 'Use get_rental_prices when a user asks about rent costs in a specific area. Use list_affordable_housing_programs when they ask about government housing schemes or subsidies. Use get_housing_finance_options when they ask about mortgages or loans.' This disambiguates tool selection.