Single tool 'search_kb' has a clear action verb and reasonable description (89 chars), but lacks critical depth. Input schema is present with two parameters (query, top_k) both typed and described, meeting baseline requirements. However, output schema is completely undocumented, the tool returns search results but LLMs cannot see what fields to expect, violating pattern:tool and pattern:response-shaper. No error handling guidance, no pagination info despite top_k parameter suggesting result sets. Parameter descriptions are minimal (10-15 chars each) and lack format/constraint details. No mention of what 'hybrid (dense + sparse) retrieval' means to an LLM unfamiliar with RAG terminology.

• Document the output schema explicitly. Example: 'Returns {results: [{chunk_id, text, score, source_file}], total_chunks, search_time_ms}'. LLMs need to know what fields exist to plan downstream calls.
• Expand parameter descriptions to 50 - 100 chars each. For 'query': 'Natural language search string (e.g., "how to deploy to production"). Supports multi-word phrases and boolean operators.' For 'top_k': 'Number of results to return (1 - 50, default 5). Higher values increase latency but improve coverage.'
• Clarify 'hybrid retrieval' in the tool description. Add: 'Uses both semantic (dense vector) and keyword (sparse BM25) search, then ranks results by relevance. Best for technical documentation and knowledge bases.'
• Add error handling guidance to the description: 'Returns empty results if no matches found. Raises error if knowledge base is unavailable or query is malformed (e.g., unmatched quotes).'
• Consider adding a 'min_score' parameter (optional, default 0.0) to let agents filter low-confidence results, improving result quality for downstream use.