Single tool 'prompt' has a basic description (67 chars, within 10-1024 range) but lacks critical depth. Input schema is minimal: only 'prompt_s' string with a 47-char description. No output schema documented. No error handling guidance, no parameter constraints, no idempotency markers. Tool name 'prompt' is generic and does not follow verb_noun convention (should be 'generate_text' or 'call_gemini'). No pagination, no structured output specification, no recovery guidance for API failures. Baseline: 194 chars avg description, 72 chars avg param annotation, this tool is below both. No tool annotations (readOnlyHint, destructiveHint, idempotentHint). Composition is minimal: single tool does one thing but lacks the scaffolding for reliable agent use.

• Rename 'prompt' to 'generate_text_with_gemini' or 'call_gemini_model' to follow verb_noun convention and clarify intent.
• Expand tool description to 150-250 chars: explain what Gemini model is used, when to call this vs other LLM tools, what output format to expect, and any rate limits or prerequisites.
• Add explicit output schema: document that the tool returns a string (the generated text), specify max length, and note that output may include citations or grounding info from Google Search.
• Enhance parameter description for 'prompt_s': specify expected length (e.g. 1-10000 chars), note that it accepts free-form text, and add a hint like 'For best results, provide clear instructions or context.'
• Implement structured error handling: catch API errors and return actionable messages like 'Gemini API rate limit exceeded. Retry in 60 seconds.' or 'Invalid API key. Check GEMINI_API_KEY environment variable.'