Five tools with basic descriptions and schemas, but significant gaps in parameter documentation and output structure. Tool names follow verb_noun convention (save_, search_, list_, delete_, get_). Descriptions exist but are brief (avg ~60 chars, below 194-char baseline). Input schemas present with types, but parameter descriptions are minimal or missing. No output schemas documented. Error handling returns plain strings rather than structured guidance. Duplicate detection and topic organization are implemented, but composition and chaining support are weak.

• Document output schemas for all tools. E.g., search_research_data should return {results: [{content: string, similarity: number, doc_id: string, metadata: object}], total_count: integer, topic: string} so LLMs can extract and chain results.
• Expand parameter descriptions to 50 - 100 chars each. For 'content' in save_research_data, add: 'Array of text strings to embed and store. Each string should be 10 - 10000 characters. Duplicates (by MD5 hash) are skipped automatically.'
• Implement structured error responses. Return {error: string, error_code: string, recovery_suggestion: string, retryable: boolean} instead of plain strings. E.g., 'Topic not found. Try list_research_topics() to see available topics.'
• Add dry-run support to delete_research_topic. Accept optional 'confirm: boolean' param. If false, return {topic: string, document_count: integer, estimated_size_mb: float, warning: 'This will permanently delete all data'}. Require confirm=true to execute.
• Return pagination metadata from list_research_topics. Include {topics: [string], total_count: integer, has_more: boolean} so agents can handle large topic lists and chain to get_topic_info for details.