This server has significant definition quality gaps. Tool names lack clear action verbs (spec_interrogate, spec_compile, architecture_decide are vague). Descriptions are present but generic (10-50 chars for most tools). Input schemas are visible but lack depth: parameters have types and basic descriptions, but many lack constraints (enums, ranges, patterns). Output schemas are completely undocumented, no tool specifies what it returns. Error handling is absent from all tool definitions. The server appears to route to LLM-based assistants rather than deterministic APIs, which makes schema validation and error recovery impossible to assess. Tool composition is poor: product_spec_assist and product_spec_connect appear to be meta-tools that duplicate or wrap other tools' functionality.

• Add explicit output schemas to all 8 tools. Document the structure, field types, and what data the LLM should expect. Example: spec_interrogate should return {gaps: string[], completeness_score: number, recommendations: string[]}.
• Rename tools with clear action verbs: spec_interrogate → analyze_product_spec, spec_compile → generate_product_spec, architecture_decide → recommend_architecture, ui_translate → convert_ui_to_design_spec, debug_guide → diagnose_product_issue.
• Expand descriptions to 100-200 chars. Include WHEN to use (vs similar tools), WHAT it returns, and any prerequisites. Example: 'Analyze a product idea against a completeness checklist. Returns gaps, a score (0-100), and recommendations. Call this first to identify missing requirements before architecture_decide.'
• Add parameter constraints: product_idea (min 10, max 2000 chars), platform (enum: web, mobile, desktop, hybrid), expected_users (enum: <100, 100-1k, 1k-10k, 10k+). Use descriptions to state these rules.
• Implement error handling: return {success: boolean, error?: {code: string, message: string, recovery_hint: string}, data?: object}. Classify errors as retryable (rate limit, timeout) vs user-fixable (invalid platform) vs fatal (auth failure).