Three tools with clear verb-noun naming (search_devices, get_device_details, get_device_manual) and adequate descriptions (100-150 chars each). All tools have input schemas with Zod validation and parameter descriptions. However, output schemas are not formally documented, responses are text-only without structured field definitions. Error handling is minimal (no recovery guidance or actionable error messages). No tool annotations (readOnlyHint, etc.). Parameter descriptions are basic but present. Tools follow single-responsibility principle. Baseline: avg tool description ~120 chars (within 10-1024 range), all params described, but lack of output schema documentation and error guidance limits score to fair range.

• Document output schemas for all tools. Define structured response objects with typed fields (e.g., search_devices returns {devices: [{model, name, brand, category, description}], total_count, has_more}). This enables LLMs to extract data reliably.
• Add recovery guidance to error responses. When a device is not found, return: 'Device not found. Try search_devices() with a partial model number or brand name.' This guides agent self-correction.
• Add tool annotations via MCP SDK. Mark all three tools with readOnlyHint=true since they only read data. This signals to agents that retries are safe.
• Enhance parameter descriptions with format and example constraints. For search_devices query param: 'Search term (model number, brand, or category name). Supports partial matches. E.g., "Samsung", "SM-G991B", "smartphone".'
• Implement pagination for search_devices. Add limit (default 20, max 100) and offset parameters. Return total_count and has_more in response so agents can iterate through large result sets.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry