This MCP server has critical definition quality gaps. All 6 tools have descriptions in Chinese (non-English), which violates LLM-optimization baselines. Input schemas are present but lack proper JSON Schema structure, parameters are documented as simple type/description pairs without formal constraints (no enums, ranges, or patterns). No output schemas are documented. Parameter descriptions are minimal (10-30 chars), well below the 72-char baseline. Tools lack error handling guidance, recovery paths, and actionable error messages. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite all being marked READ_ONLY. The framework appears to be a RAG/ML system repurposed as an MCP server with health/fitness domain tools, but the tool definitions are skeletal.

• Translate all tool descriptions and parameter descriptions to English. Use 50-200 character descriptions that explain WHAT the tool does, WHEN to use it, and what it returns. Example: 'Checks if user health profile has contraindications for a list of exercises. Returns a list of contraindicated exercises with severity levels (low/medium/high). Call this before recommending any exercise plan.'
• Document output schemas for all tools. Specify the structure, field types, and what data is returned. Example: 'Returns {contraindicated_exercises: [{exercise_id: string, exercise_name: string, severity: enum(low|medium|high), reason: string}], safe_to_proceed: boolean}'
• Expand parameter descriptions to 50-100 characters with format/constraint details. Example: 'user_profile (dict): User health data including age (int), weight (float kg), medical_conditions (list of strings), injury_history (list). Required fields: age, weight.'
• Add tool annotations to all tools. Since all are READ_ONLY, add readOnlyHint: true to each tool definition. This signals to agents that these tools are safe to call without side effects.
• Add error handling guidance to descriptions. Example: 'If user_profile is missing required fields, returns error with list of missing fields. If exercise_id not found, returns error with available exercise IDs. Retry with corrected input.'

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry