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sanhao-global-experts

ootd-stylist-pro

AI-powered restaurant & brand intelligence pack. Instantly generate a ready-to-use ootd stylist pro workflow - no prompt engineering needed. Ships as a full prompt kit with step-by-step instructions and a 30-day support promise.

Input Schema

NameRequiredDescriptionDefault
queryNoOptional user query to scope the analysis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2/5.0
Behavior2/5

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

No annotations exist, so the description carries the full behavioral burden and fails it: 'ships as a full prompt kit with step-by-step instructions and a 30-day support promise' is commercial copy, not behavior. It never says whether the call mutates state, what the invocation produces (a prompt template? an analysis? a file?), or how the optional query influences output.

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

Conciseness2/5

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

Three short sentences, but every one is marketing ('no prompt engineering needed', '30-day support promise') and none is front-loaded with usable capability information. Brevity here is under-specification rather than conciseness.

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

Completeness2/5

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

For a single optional-parameter tool with no annotations and no output schema, the description should at minimum say what invoking it returns and how output relates to the query. Instead it promises a 'prompt kit' and support terms, leaving the actual call semantics entirely undefined.

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 100% and the single optional 'query' parameter is documented in the schema as scoping the analysis, so the baseline is 3. The description adds no meaning about what the query actually scopes, but with full schema coverage that omission is not penalized heavily.

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

Purpose2/5

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

The description opens with 'AI-powered restaurant & brand intelligence pack', which has nothing to do with the tool name ootd-stylist-pro, then says it will 'generate a ready-to-use ootd stylist pro workflow'. The two halves describe different domains, and neither names a concrete verb+resource an agent could act on (generate? retrieve? run?). It reads as product packaging copy restating the name rather than a statement of function.

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

Usage Guidelines1/5

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

There is no when-to-use guidance, no prerequisites, and no reference to alternatives despite many close siblings such as outfit-stylist, makeup-for-face, and style-adjacent kits. Worse, the 'restaurant & brand intelligence pack' framing actively misroutes an agent looking for F&B or brand tools.

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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