Descriptions are present but minimal (10-50 chars), below the 50-200 char LLM-optimized baseline. No parameter descriptions exist (N/A since no params). No output schemas documented. No error handling guidance. Tools lack composition hints (e.g., which joke APIs are available, rate limits, fallback behavior). No security considerations (external API calls unvalidated). Naming is acceptable (verb_noun pattern: get_*) but descriptions are too terse to guide LLM selection.

• Add explicit input schemas to all tools. Even if no parameters are required, declare parameters: { type: 'object', properties: {}, required: [] } to signal intentional design.
• Expand descriptions to 80-150 chars. Example: 'get-chuck-joke' → 'Fetch a random Chuck Norris joke from the public API. Returns a single joke string. Use when user asks for humor or entertainment.'
• Document output schemas in tool definitions. Example: { type: 'object', properties: { joke: { type: 'string', description: 'The joke text' } }, required: ['joke'] }.
• Add error handling: wrap fetch() calls in try-catch, return structured errors with recovery hints (e.g., 'API timeout. Retry in 5 seconds.' or 'Rate limited. Try a different joke type.').
• Add timeout (5-10s) and retry logic to fetch() calls. Log failures to stderr for debugging. Consider circuit breaker pattern if external APIs are unreliable.