Two tools with complete schemas and descriptions, but descriptions are generic and lack LLM-optimized guidance. Tool names follow verb_noun pattern (list_, search_) which is good. However, descriptions lack actionable context about when to use each tool, what prerequisites exist, or how results chain together. Parameters are typed but lack detailed constraints (e.g., threshold range 0-1 is mentioned but not enforced in schema). No output schema documentation. Error handling returns text but lacks recovery guidance. Overall: functional but below production baseline.

• Expand tool descriptions to 80-150 characters. Example for search_knowledge: 'Search Cherry Studio knowledge bases using natural language. Returns ranked document snippets with similarity scores. Call list_knowledge_bases first to see available bases. Useful for RAG, fact-checking, and context retrieval.'
• Add JSON Schema constraints: threshold with minimum: 0, maximum: 1; top_k with minimum: 1, maximum: 100 (or server limit). This prevents invalid LLM inputs.
• Document output schema explicitly. E.g., search_knowledge returns array of {score: number 0-1, source: string, content: string}. list_knowledge_bases returns array of {name, path, sizeMb, vectorCount, dimension}.
• Add recovery guidance to error responses. E.g., 'Knowledge base "xyz" not found. Available bases: [list]. Try search_knowledge with a different kb_name.' This enables agent self-correction.
• Add pagination: search_knowledge should accept limit (default 10, max 50) and return {results: [...], total_count: number, has_more: boolean}. Prevents context window exhaustion.