KaaS exposes a single tool 'ask' with a complete JSON Schema input definition and a clear, actionable description (89 chars). The tool accepts natural-language queries grounded in optional wiki paths, with model and knowledge-base overrides. Schema includes proper type declarations and parameter descriptions. However, the output schema is not documented, LLMs cannot predict the response structure (cited answer format, fields, pagination). No error handling guidance is provided. The tool is read-only (low risk), but lacks recovery hints for common failures (e.g., 'no relevant articles found'). Naming is generic ('ask') rather than verb-noun ('query_knowledge_base' or 'search_wiki'). Overall: solid foundation, but incomplete for production LLM integration.

• Document the output schema explicitly. Example: 'Returns {answer: string, citations: [{path: string, excerpt: string}], model_used: string, tokens_used: {input: int, output: int}}'. This enables LLMs to extract and chain data.
• Rename tool from 'ask' to 'query_knowledge_base' or 'search_wiki' to follow verb_noun convention and improve LLM discoverability.
• Add error handling guidance to the tool description: 'If no articles match, returns error with suggestions to broaden query or specify paths. If model fails, returns error with fallback model recommendation.'
• Constrain 'model' parameter: either provide an enum of supported models (e.g., ['gpt-4o', 'gpt-4-turbo', 'claude-3-opus']) or document the format and validation rules in the description.
• Clarify 'paths' parameter format: 'Optional list of wiki article paths (e.g., ["docs/api/overview", "docs/api/auth"]). If omitted, searches the master index. Paths are case-sensitive and must exist in the knowledge base.'