Single tool 'generate' has critical definition gaps. Description is minimal (32 chars, below 20-char floor). Input schema visible but lacks proper parameter descriptions and type clarity. No output schema documented. Parameters 'inputs' and 'parameters' lack descriptions explaining their purpose, constraints, or expected format. No error handling guidance. Tool does not follow verb_noun naming (should be 'generate_text' or similar). Overall structure suggests early-stage implementation without LLM-optimization patterns.

• Expand tool description to 50 - 150 characters explaining: what the tool generates (text completions, summaries, etc.), when to use it (e.g., 'Use to generate creative text responses from a prompt'), and what it returns (a single text response). Example: 'Generates a text response from a given prompt. Use for creative writing, summarization, or open-ended text generation. Returns a single completion string.'
• Add descriptions to all parameters: 'inputs' → 'The prompt or input text to generate a response from (required, string, max 2000 chars)'; 'parameters' → 'Optional configuration dict controlling generation behavior (e.g., {"temperature": 0.7, "max_tokens": 100}). Defaults to empty dict.'
• Document the output schema explicitly in the tool definition or response model docstring: 'Returns {"choices": [{"index": 0, "message": {"content": "<generated text>"}}]}. The content field contains the generated response string.'
• Rename tool from 'generate' to 'generate_text' to follow verb_noun convention and improve clarity for LLM tool selection.
• Add error handling: catch timeouts, invalid inputs, and API failures; return structured errors like '{"error": "Generation timeout after 30s. Try again or reduce max_tokens."}' with actionable recovery guidance.