Three tools with explicit schemas and descriptions, but significant quality gaps. Tool names lack clear action verbs (product_list vs list_products, product_delete_translation vs delete_product_translation). Descriptions are present but generic and lack actionable context. Parameter descriptions are minimal. No output schemas documented. Error handling is basic. The server exposes GraphQL implementation details rather than abstracting to a clean agent interface.

• Rename tools to start with action verbs: list_products, delete_product_translation, create_or_update_glossary_item. This matches the 90% baseline of A+ tools and helps LLMs parse intent from the name alone.
• Expand tool descriptions to 50 - 200 characters. Example: 'list_products: Retrieve paginated product list with optional filtering by identifier. Returns product IDs, codes, status, and translations. Use this to discover products before updating or deleting.' This answers WHAT, WHEN, and WHAT-RETURNS.
• Document output schemas for each tool. Specify that responses include {content: [{type: 'text', text: string}], isError: boolean}. Include sample response structures so LLMs know what fields to expect.
• Replace example values in parameter descriptions with formal constraints. Instead of 'e.g. ABC-123, 1456', use a regex pattern or description: 'Alphanumeric client ID (2 - 20 chars, case-insensitive).'
• Add a dry-run or confirmation step to product_delete_translation. Return a confirmation token that must be passed in a second call, or add a 'confirm: true' parameter that defaults to false. This prevents accidental data loss.