This server implements 4 tools with basic Zod schemas and descriptions. All tools are read-only and follow simple parameter patterns. However, descriptions are brief (18-63 chars, below the 194-char baseline), parameter descriptions lack depth, output schemas are not documented, and error handling returns JSON strings rather than structured error objects. The tools are well-named with verb-noun convention (search, fetch, fetch_paginated, vector_store_status), but the overall quality falls short of production grade. Tool composition is reasonable, search+fetch pair enables discovery and retrieval, but the lack of pagination metadata (next_cursor, total_count) in fetch output and undocumented output schemas limit agent planning.

• Expand tool descriptions to 100 - 200 characters. Template: 'Search [resource type] for documents matching [query type]. Use this to discover available docs before calling fetch. Returns a list of [5-20] results with IDs and titles. Requires [prerequisites, if any].'
• Document the output schema for each tool in code comments. Example: '// Returns { results: Array<{ id: string, title: string, relevance: number }>, total: number }'
• Add explicit constraints to fetch_paginated parameters: cursor ≥ 0, max_tokens ∈ [100, 50000]. Include in parameter description: 'Character offset (0-based, defaults to 0). Must be non-negative.'
• Replace JSON.stringify() error responses with structured error objects: { error: { code: 'SEARCH_FAILED', message: '...', retryable: true, recovery: 'Try with a simpler query' } }
• Add parameter descriptions explaining where to get IDs. Example for fetch: 'Document ID from search results (e.g., 'docs/api-reference/v1'). Call search() first to discover available IDs.'
• For fetch, document whether the response is complete or truncated. If large, suggest fetch_paginated. Return metadata: { id, title, text, complete: boolean, bytes: number }
• Add a list_documents tool to help agents discover available docs without blind search. Improves discoverability.
• Document the vector store's index structure and update frequency in vector_store_status response. Example: '{ configured: true, store_id: '...', indexed_docs: 1024, last_updated: '2024-01-15T10:00Z' }'
• Consider adding a get_document_metadata tool that returns id, title, url, size, and last_modified without fetching content. Lightweight discovery.