Neurocache provides 4 well-named tools with clear descriptions (avg 180 chars, within baseline 194). All tools have input schemas with types and descriptions. Naming follows verb_noun convention (search_, get_, list_, save_). However, schema completeness varies: search_knowledge_base and get_document have full parameter details, but save_to_knowledge_base lacks validation constraints (no enum for content_type despite listing valid values). Output schemas are not documented in the source. Error handling is minimal, no recovery guidance or actionable error messages visible. The knowledge base tools form a coherent chain (search → get → save), but missing output documentation and error patterns prevent higher scores.

• Add formal output schemas to all tool definitions. Document the structure of search_knowledge_base results (e.g., {results: [{path, similarity, content, source_number, obsidian_url, content_type, section_header}], total_count}). This enables LLMs to extract and chain data correctly.
• Convert content_type parameters to enums. Replace 'One of: personal_note, book_note, chat_insight' with a formal enum constraint in the schema. This prevents hallucinated values and is machine-parseable.
• Add min/max constraints to numeric parameters. Set top_k to minimum 1, maximum 20 in the schema. Add bounds to any other numeric params (e.g., page size 1 - 100).
• Implement error recovery guidance. When get_document fails with 'not found', return 'Document not found. Try search_knowledge_base() with keywords from the title.' When save_to_knowledge_base fails on invalid content_type, return the list of valid options.
• Document pagination for list_documents and search_knowledge_base. Add limit and offset/cursor parameters, return total_count, and cap default results at 20. State the limit in tool descriptions.