Single tool 'recommend_models' has a basic schema with one string parameter, but lacks depth in descriptions and error guidance. The tool description (54 chars) is below the 194-char baseline and doesn't explain WHEN to use it or what makes it distinct. Parameter description is adequate (72 chars, at baseline) but generic. No output schema is documented, LLMs cannot predict the structure of recommendations. Error handling returns plain text rather than structured guidance. No tool annotations (readOnlyHint, etc.). Overall: functional but minimal quality.

• Expand tool description to 150 - 250 characters. Include: (1) What it does: 'Recommends AI models based on a natural-language use case description.' (2) When to use it: 'Call this when the user asks for model suggestions or comparisons.' (3) What it returns: 'A ranked list of models with reasoning, cost estimates, and tradeoffs.'
• Document the output schema explicitly in the tool description or as a separate schema field. Example: 'Returns an array of objects with fields: model_id (string), rank (integer 1 - N), rank_label (string: 'top', 'good', 'fair'), reasoning (string), cost_estimate (string), tradeoffs (string), dataset_age_hours (integer).'
• Enhance error handling with recovery guidance. Instead of 'Error: ANTHROPIC_API_KEY environment variable is required', return 'Configuration error: ANTHROPIC_API_KEY not set. This is a server-side issue, contact the administrator. The request cannot be retried until the key is configured.'
• Add tool annotations to the schema: set readOnlyHint=true and idempotentHint=true to signal that this tool is safe to call multiple times without side effects.
• Rename tool to 'search_ai_models_by_usecase' or 'get_model_recommendations' to clarify the action and distinguish it from other model-related tools.