Single tool with clear purpose and good description (194 chars, within baseline 34-392 range). Input schema present with type and description. However, output schema is undocumented, LLMs cannot predict the markdown structure or plan downstream processing. No error handling guidance, no parameter constraints, and no indication of side effects (cost warnings, API rate limits, timeouts). Tool is well-intentioned but lacks production-grade robustness.

• Document output schema explicitly: return a structured object with fields {title: string, summary: string, body_markdown: string, citations: [{source_title: string, source_url: string, fact: string}], known_gaps: [string], revision_count: int} instead of free-form markdown. This enables LLMs to extract and chain data reliably.
• Add input validation: constrain 'question' to 10-500 characters, reject empty/whitespace-only input, and return actionable error messages like 'Question too short (got 3 chars, need 10+). Provide a research question with at least 10 characters.'
• Elevate cost and timeout warnings into the tool description itself: 'WARNING: This tool makes multiple LLM and web search API calls and typically takes 60+ seconds. Costs ~$0.50-$2.00 per call. Use sparingly.' This ensures LLMs see the implications before invoking.
• Add error classification guidance: document which failures are retryable (network timeout, rate limit) vs. fatal (invalid API key, malformed question). Return error responses like {error: 'timeout', message: 'Research pipeline exceeded 120s timeout. Retry with a narrower question.', retryable: true}.
• Consider a dry-run or preview mode: add an optional 'preview_only' parameter (default false) that returns an outline of sub-questions and sources without running the full pipeline. This lets agents validate the research plan before committing to a long, expensive run.