This is a Vercel AI SDK tool library, not an MCP server. Tools are well-named with action verbs (search, resolve, list, get) and have solid descriptions (avg 150 chars). All 5 tools have complete Zod schemas with typed parameters and descriptions. However, output schemas are not formally documented, responses are inferred from code. Error handling is minimal (generic HTTP errors). The library is production-ready for Vercel AI SDK integration but lacks MCP-specific patterns like tool annotations and structured error recovery.

• Document output schemas explicitly. Add JSDoc @returns blocks or TypeScript type exports so LLMs and clients can see expected response fields. Example: '@returns {SearchResult} { hits: SearchHit[], total: number, page: number, limit: number }'
• Enhance error handling with categorization and recovery hints. Replace generic HTTP errors with: 'API error (retryable): {status}. Try again in 5 seconds.' or 'Invalid query: {reason}. Ensure query is 1-100 characters.'
• Add tool annotations to all tools. Use Zod metadata or MCP tool registration to declare readOnlyHint: true and idempotentHint: true for all 5 tools, enabling client-side optimizations.
• Improve listProtocols and listRegistries descriptions. Add: 'Returns a list of all supported agent protocols/registries. Call this first to understand available options before searching.' Include expected result size.
• Add per-parameter validation and error messages. Validate query length (1-100 chars), limit range (1-100), page >= 1. Return actionable errors: 'Invalid limit: 500. Must be 1-100.' instead of generic failures.