This server defines 4 tools with basic schemas and minimal descriptions. All tools are explicitly registered in index.ts with Zod schemas, so tool definitions are clearly visible. However, descriptions are critically sparse (13-32 chars, well below the 10-1024 baseline recommended minimum of 50-200 for LLM clarity). The 'test' tool lacks any input schema entirely. Parameter descriptions exist for 'echo' and 'random-number' but are generic. Output schemas are not documented. Error handling is minimal, tools return only happy-path responses with no guidance for failure modes. No input validation beyond Zod type coercion. This is a proof-of-concept or demo server, not production-grade.

• Expand each tool description to 50-200 characters. Example: 'test' → 'Verify MCP server is responding and authentication is valid. Returns success message if JWT token is accepted.' (104 chars). Example: 'get-time' → 'Retrieve the current server time in ISO 8601 format. Useful for timestamping operations or verifying server clock synchronization.' (135 chars).
• Add a non-empty input schema to the 'test' tool. Even a simple {} is correct, but consider adding an optional 'verbose' boolean parameter to control output detail: {verbose: z.boolean().optional().describe('If true, include server uptime and version info')}
• Document output schemas explicitly. Add a comment or JSDoc above each tool handler describing the return structure, e.g.: '// Returns {content: [{type: "text", text: string}]}' and mention the structure in the description.
• Add explicit bounds validation to numeric parameters. For 'random-number', add: 'min and max must be integers between -999999 and +999999. If min >= max, min defaults to 0 and max to 100.' Then validate in the handler: if (min >= max) throw new Error('min must be less than max').
• Implement error handling with actionable messages. Example for 'echo': if (!message || message.trim().length === 0) return { content: [{type: 'text', text: 'Error: message cannot be empty. Provide a non-empty string.'}] }.
• Add parameter descriptions to 'test' (if you add input params) and ensure every parameter in all tools has a clear, specific description of what it controls and why an LLM should set it.