This server exposes 25 tools across notebook, source, note, model, and search management. Tool definitions are consistently structured with descriptions and input schemas via the mark3labs/mcp-go framework. However, there are significant quality gaps: (1) Output schemas are entirely undocumented, the code registers tools with input definitions but provides no structured response documentation for LLMs to plan downstream calls; (2) Descriptions are sparse (10-80 chars typically) and lack actionable context about when to use each tool, prerequisites, or error recovery; (3) Parameters lack constraints, numeric 'limit' fields accept 1-100 but have no min/max validation in schema; (4) No error handling guidance for LLMs, failed API calls return raw HTTP errors rather than recovery hints; (5) Two tools (ask_question and ask_simple) are nearly identical, violating single-responsibility and creating LLM confusion. Naming is consistent (verb_noun), but schema completeness and description depth lag production standards. The server would benefit from response type documentation, richer descriptions, enum constraints for open fields, and error classification.

• Document output schemas for every tool. Specify field names, types, and required fields. Example: 'Returns {id: string, name: string, created_at: ISO8601, archived: boolean}'. This is critical for LLM planning and chaining.
• Expand descriptions to 100-200 chars with actionable context. Transform 'Get a specific notebook by ID' into 'Retrieve a single notebook. Use after list_notebooks to fetch detailed metadata. Returns full notebook structure including archived status and creation date.'
• Add explicit min/max bounds to numeric schemas. Change 'limit' parameter from bare number type to { type: 'number', minimum: 1, maximum: 100 } in JSON Schema, with description confirming the range.
• Consolidate ask_question and ask_simple into a single tool with an optional 'simplified' or 'mode' parameter. If conceptually different (e.g., one for simple Q&A, one for complex reasoning), document the distinction clearly in descriptions so LLMs pick correctly.
• Implement error classification in tool responses. Return structured errors: { error: string, category: 'retryable'|'user_fixable'|'fatal', suggestion: string }. Example: 'Notebook not found. Try list_notebooks() to see available notebooks.'
• Add enum constraints to limited-value parameters. Change create_source 'type' to { type: 'string', enum: ['link', 'upload', 'text'] }. Change search 'type' to { type: 'string', enum: ['vector', 'text'] }.
• Annotate tools with safety hints. Mark delete_* tools with readOnlyHint: false and destructiveHint: true. Mark list_* and get_* tools with readOnlyHint: true. This helps agents reason about side effects.