The server provides a single tool 'recommend' with a complete input schema and reasonable descriptions. However, the tool violates single-responsibility principle (combines scoring, ranking, gift messaging, and wrapping suggestions), lacks output schema documentation, and has no error handling guidance. The parameter descriptions are adequate but could be more specific about constraints and formats. The schema is well-formed with proper JSON Schema types, but output structure is inferred from code rather than formally documented.
Tools (1)
recommendread onlysource verified67/100
Generate gift recommendations. Scores products based on recipient interests, occasion, and budget fit.
Tool combines multiple responsibilities: scoring products, ranking by match, generating gift messages, and suggesting wrapping styles. Should be split into separate single-purpose tools (score_products, generate_gift_message, get_wrapping_suggestion) so agents can compose them independently.
Output schema is not formally documented. Return type structure (recommendations array with product_id, product_name, price, match_score, match_reasons, budget_fit; plus gift_message, gift_wrapping_suggestion, recipient_summary, etc.) is inferred from code only. LLMs cannot plan downstream usage without knowing expected fields.
Rename tool from 'recommend' to 'generate_gift_recommendations' (action verb + noun) to match pattern:tool-naming. Makes intent clear before LLM reads description.
Split into three separate tools: (1) 'score_gift_products', filters catalog, scores by interest/occasion/budget, returns ranked list; (2) 'generate_gift_message', returns occasion-specific message; (3) 'get_gift_wrapping_suggestion', returns occasion-appropriate wrapping style. Allows agents to invoke independently or chain as needed.
Add formal output schema documentation to each tool. For score_gift_products, document: returns {recommendations: [{product_id, product_name, price, match_score, match_reasons, budget_fit}], total_products_evaluated}. For generate_gift_message: returns {occasion, message, source}. For wrapping: returns {occasion, style_description}.
Document occasion as enum constraint in parameter: 'occasion (enum): one of [birthday, wedding, holiday, graduation, anniversary]. Determines gift message style and scoring weights.'
Add recipient.interests documentation: 'List of recipient interests as free-form strings (e.g. ["fitness", "cooking", "travel"]). Tool maps to known categories: fitness, beauty, travel, tech, cooking, reading, gaming, fashion, home, wellness. Unrecognized interests are silently ignored.' OR provide a discover_interests tool.
Document scoring algorithm in tool description: 'Scores each product 0-75 pts: up to 40 for interest match, up to 20 for category fit, up to 15 for budget fit (70-95% of budget is optimal), occasion bonuses. Products below budget or scoring 0 are excluded. Results ranked by score descending. Top N returned.'
Score history
Overall score trend
First recorded score · v2 rubric
58/100
Scored
Grade
Overall
Spec posture
Rubric
2026-09-23
D
58
2026-07-28+
v2
No error handling guidance. Tool lacks recovery instructions for edge cases: What if product_catalog is empty? What if no products fit budget? What if interest_categories yield no matches? Responses should tell LLM next steps.
recipient.interests parameter accepts free-form array of strings with no validation. Should document expected interest values or reference a discovery tool to list valid interests. Current implementation silently ignores unrecognized interests (e.g. 'skydiving' may not match any category).
occasion parameter accepts free-form string. Should be enum (birthday|wedding|holiday|graduation|anniversary) to prevent hallucinated occasion values. Code defaults to 'default' key for unrecognized occasions, hiding input errors.
product_catalog.items lacks format constraints. Each product object should document required vs optional fields (id, name, price required; category, tags optional). Current schema marks all as present but implementation handles missing fields gracefully with defaults, inconsistent contract.
Scoring algorithm hardcoded in _score_product() has no documentation. LLMs cannot predict how 'match_score' is calculated (40 pts for interest, 20 for category, 15 for budget fit, -5 if too cheap). Undocumented scoring leads to agent misexpectations about recommendation quality and ranking.
recommend
Add error handling guidance: 'If product_catalog is empty or no products fit budget and interests, returns {recommendations: [], explanation: "No products matched criteria. Try expanding budget, broadening interests, or checking catalog contents."}', tells LLM what to do next.
Validate occasion input. If not one of [birthday, wedding, holiday, graduation, anniversary], return error: 'Invalid occasion "{input}". Must be one of: birthday, wedding, holiday, graduation, anniversary.' Prevents silent fallback to default.
Document required vs optional product fields: 'Each product in product_catalog must have {id (string, required), name (string, required), price (number, required), category (string, optional), tags (string[], optional)}. Missing category/tags are treated as empty sets.'
Add parameter constraints to schema: budget_usd minimum 0.01, top_n minimum 1 maximum 100. Include in description: 'top_n (integer, default 5): number of top recommendations to return. Range: 1 - 100.'
Add idempotent flag. Since tool purely computes recommendations from static inputs (no state mutation), document in tool description: 'This tool is idempotent, repeated calls with identical inputs return identical results.' Allows agents to safely retry on transient failures.