Server has 5 tools with basic descriptions and partial schemas. Tool names follow verb_noun convention (scrape_*, search_*, score_*), which is good. However, parameter descriptions are minimal or missing context. Input schemas are present but lack detail on constraints, ranges, and validation rules. Output schemas are not documented. Error handling is absent, no guidance on recovery or retry logic. The style-scorer tool references external knowledge objects without documenting their structure. Overall, definitions are functional but lack the rigor expected for production use.

• Document output schemas for all tools. For scrape_products, specify: { success: boolean, site: {title, url, description}, rawProducts: [{text, link, image}], count: number, inlineImages: [{index, productText, data, mimeType}], instruction: string }. For score_and_rank_products, specify the ranked product array structure with score breakdowns.
• Add min/max constraints to numeric parameters. E.g. maxItems: {type: 'number', minimum: 1, maximum: 100, description: '...'}, scrollCount: {minimum: 0, maximum: 10}, maxResults: {minimum: 1, maximum: 50}.
• Add error handling examples in descriptions. E.g. for scrape_products: 'If the URL is invalid or unreachable, returns {success: false, error: "..."}. Verify the URL is accessible and the site allows scraping. Try a different URL or check network connectivity.'
• Consolidate search_x and search_trends_x into a single search_fashion_trends tool with a fallback_only boolean parameter. Or clearly document when to use each (search_x for direct X API queries, search_trends_x only when X API is unavailable).
• Document the 'knowledge' parameter structure for score_and_rank_products with a JSON schema example showing required fields: skeleton, color, face, body, coordination, taste, each with their expected sub-fields.