This is a template repository with stub implementations, not a production MCP server. Both tools have basic schemas and descriptions, but critical gaps undermine quality: (1) descriptions are minimal (18-47 chars, well below the 50-200 char baseline for LLM optimization); (2) parameter descriptions are sparse or missing (e.g., 'status' in list_campaigns lacks description in Node template; 'campaign_id' in get_metrics has no description); (3) output schemas are completely undocumented, no indication of what fields are returned or their types; (4) error handling is absent, stub implementations return empty JSON with no guidance on failure modes; (5) tool implementations are TODO placeholders with no actual API integration. The Python template is slightly better (status has a default), but both fall far short of production readiness.

• Expand tool descriptions to 50-200 characters. Example: 'list_campaigns' → 'List all advertising campaigns for an account, filtered by status (active, paused, or all). Returns campaign ID, name, status, and key metrics (impressions, clicks, spend).'
• Add descriptions to every parameter. For 'status': 'Campaign status filter: active (currently running), paused (temporarily stopped), or all (both active and paused).' For 'campaign_id': 'Unique identifier of the campaign to retrieve metrics for.'
• Document output schemas explicitly. Specify what fields are returned (e.g., campaigns array with id, name, status, impressions, clicks, spend) and their types.
• Add error handling with recovery guidance. E.g., 'If account_id is invalid, call list_accounts() first to find the correct ID.' Categorize errors as retryable vs. user-fixable.
• Replace TODO implementations with actual API calls. Integrate with at least one advertising platform (Google Ads, Meta, Microsoft) to demonstrate real functionality.