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Noteboxd Fragrance MCP (remote)

ai_recommend

Read-onlyIdempotent

Get personalized fragrance recommendations based on your taste profile. Optional natural-language prompt to guide results (e.g. 'something fresh for summer'). Requires Pro.

See also: ai_wear_today for what to wear right now · user_get_cabinet for your collection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
promptNoOptional natural-language guidance, e.g. 'fresh for summer office'
contextYesExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
response_formatNomarkdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the description only needs to add context. 'Requires Pro' is a genuinely useful entitlement disclosure beyond the annotations, though it says nothing about the result set size beyond the schema's limit cap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences plus a routing line; the core purpose is front-loaded and every clause (taste profile, optional prompt, Pro gate, sibling routing) earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the definition leaves the return shape (what a recommendation contains, ordering, effect of limit) unstated, and half the parameters lack semantics. Annotations and the schema's limit cap partially cover this, making it adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50%: prompt and context are documented in the schema, while limit and response_format carry no descriptions. The description's prompt example ('something fresh for summer') largely duplicates the schema's own example and it never addresses limit or the markdown/json response format, so it does not compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Get) plus resource (personalized fragrance recommendations) and the basis (taste profile), and the 'See also' line explicitly separates it from ai_wear_today and user_get_cabinet. An agent can place this tool relative to its siblings without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes to two alternatives with their distinct purposes ('what to wear right now', 'your collection') and flags the Pro entitlement prerequisite. It does not state when this tool is a poor choice, so it falls short of the full when/when-not bar.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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