Single tool 'semantic_search' has a well-structured schema with proper JSON Schema definition, clear parameter descriptions, and a detailed multi-line description in Japanese and English. Input schema includes type constraints (top_k: 1-100, default 10). Output schema is documented via SemanticSearchOutput Pydantic model. However, the tool lacks error handling guidance, no recovery hints for common failure modes, and no explicit mention of prerequisites or when to use it vs alternatives. Description is 450+ chars (exceeds 200-char baseline for LLM optimization). Parameter descriptions are present but could be more concise. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite being a read-only operation.

• Add readOnlyHint=true to semantic_search Tool definition to signal safety to agents.
• Condense tool description to ~150 chars: 'Search Danbooru tags semantically from natural language queries. Returns top_k related tags for SDXL anime prompt generation.'
• Add error handling in call_tool(): catch DatabaseNotInitializedError and return actionable message: 'Database not initialized. Run: python -m danbooru_rag.builder to build index.'
• Document SearchResult schema in code comments or docstring so agents understand what fields are returned (tag name, similarity score, etc.).
• Add query length validation: reject queries <3 chars with message 'Query too short. Use at least 3 characters for semantic matching.'

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

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