14 tools with mixed quality. Google Analytics tools (1-5) have clear names and basic descriptions but lack output schema documentation. Business analyst tools (6-12) have good parameter descriptions but vague, generic descriptions (e.g., 'Summary stats for pasted price lists'). Memory tools (13-14) have solid descriptions. Critical gaps: no visible output schemas for any tool, no error handling guidance, no parameter validation rules (e.g., days range, limit bounds), and no tool annotations. Average tool description ~120 chars (below 194-char baseline). Parameters present but lack constraints (enums, ranges). No evidence of idempotency or retry guidance.

• Document output schemas for all tools. Example for google_analytics_get_traffic: {"sessions": integer, "users": integer, "pageViews": integer, "bounceRate": number, "period": string}. This enables LLMs to chain tools and extract required fields.
• Add parameter constraints: days (1 - 365), limit (1 - 100), property_id (required, format: 'properties/[0-9]+'). Use JSON Schema minValue, maxValue, pattern, and enum fields.
• Expand business analyst tool descriptions to 100 - 150 chars. Example: 'ba_price_series_summary: Analyze a list of prices to compute mean, median, std dev, min, max. Use this to understand price distribution before setting pricing strategy.'
• Add error handling guidance to each tool description. Example: 'If property_id is invalid, the tool returns error code 400 with message "Invalid property ID format". Verify the ID matches properties/[0-9]+ and retry.'
• Add tool annotations via MCP schema: mark google_analytics_* tools with readOnlyHint=true, mark save_memory with destructiveHint=true (modifies state), mark all tools with idempotentHint=true if safe to retry.