Single tool 'search_qa' has a basic description (54 chars) but lacks critical depth. Input schema is present with a 'query' parameter (string type, 72-char description), meeting minimum requirements. However, the description does not explain WHEN to use this tool, what it returns structurally, or how it handles failures. No output schema is documented. The tool description is generic and does not guide LLM selection. Parameter description is adequate but lacks format/constraint guidance. No error handling guidance provided. Tool does one thing (search), but lacks production-grade polish expected for agent integration.

• Add structured output schema: return {"answer": string, "question": string, "found": boolean, "confidence": float} instead of bare string. This lets LLMs verify success and extract fields reliably.
• Expand tool description to 150-200 chars: 'Search a Q&A database for answers to user questions. Performs case-insensitive substring matching on question text. Returns the first matching answer or indicates no match. Use when the user asks a factual question about predefined topics.'
• Implement error classification: return {"status": "not_found", "message": "No answer found for query: ...", "suggestion": "Try a simpler keyword or check the available topics."} to guide LLM recovery.
• Add parameter constraints to description: 'query (string, 1-500 chars): Natural language question or keyword phrase. Matching is case-insensitive substring search.'
• Consider adding optional parameters: 'limit' (default 1, range 1-10) to return top-N results, and 'fuzzy' (boolean, default false) to enable fuzzy matching for typo tolerance.