The server implements 5 tools with explicit FastMCP registration and visible schemas. However, critical issues prevent a higher score: (1) Parameter descriptions are present but generic and lack actionable constraints (e.g., 'content' is described as 'List of text content to save' with no guidance on format, length, or expected size); (2) Output schemas are undocumented, tools return free-form strings rather than structured objects, forcing LLMs to parse unstructured text; (3) Error handling returns status messages but provides no recovery guidance or error classification; (4) The save_research_data and delete_research_topic tools are destructive but lack confirmation/dry-run patterns; (5) Tool names are verb-prefixed and clear, but descriptions are brief (34 - 92 chars, below the 194-char baseline) and lack 'WHEN to use' context. Per-tool scores average to 52, placing this server firmly in the 'D' range (50 - 59 Poor).

• Document output schemas for all tools. E.g., save_research_data should return {success: boolean, message: string, saved_count: integer, duplicate_count: integer, topic: string} instead of a plain string. This enables LLMs to extract and chain results.
• Add actionable parameter constraints to descriptions. E.g., 'content: List of text content to save (max 100 items, max 50,000 characters per item, UTF-8 encoded). Include a minimum item length to prevent noise.'
• Implement a dry-run or confirmation pattern for destructive tools. Add an optional 'confirm=true' parameter to delete_research_topic. If confirm is omitted, return {success: false, requires_confirmation: true, warning: 'This will permanently delete topic and all <N> documents. Call again with confirm=true to proceed.'}.
• Enhance error responses with recovery guidance. E.g., instead of 'No research data found for topic: xyz', return {success: false, error: 'topic_not_found', available_topics: ['default', 'research-2024'], message: 'Topic not found. Available topics: default, research-2024. Did you mean one of these?'}.
• Expand tool descriptions to 150 - 250 chars with WHEN-to-use guidance. E.g., 'Save research content to vector database for future retrieval. Use after collecting documents to organize by topic. Automatically detects and skips duplicates via content hashing.'
• Refactor search_research_data output from formatted string to structured object: {success: boolean, results: [{content: string, similarity_score: float, doc_index: int}], total_count: int, topic: string}.