Fetch designers by Moda Operandi department (women or men), returning live slugs valid for search.
List Moda Operandi designers. Returns the live designer facet for one department. Every returned slug is valid for modaoperandi-search.
| Name | Required | Description | Default |
|---|---|---|---|
| gender | Yes | Department |
Changes observed during successful MCP inspections.
v1.17.5Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden, and it does reasonably well: it states the tool returns a live facet, limits scope to one department, and guarantees that every returned slug is valid for modaoperandi-search. It omits output shape and pagination details, but for a simple read-only list with one enum parameter this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, both load-bearing. The main action is front-loaded, and the second sentence adds a valuable cross-tool guarantee without wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter enum-driven list tool, the description is nearly complete. It doesn't spell out the precise output structure, but the slug-validity guarantee tells the agent what to do with the results, and the absence of an output schema is partially compensated by that guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already documents gender as a department with an enum of women/men. The description's 'one department' phrasing echoes the schema but adds no new parameter-level meaning, matching the baseline for well-covered parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource — 'List Moda Operandi designers' — and clarifies the scope with 'live designer facet for one department.' The slug-validity sentence further distinguishes this tool from category/product/search siblings by defining exactly what it produces and how it can be used downstream.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended usage is clear: call with a department to get current designers, and the returned slugs are guaranteed valid for modaoperandi-search. It does not explicitly name alternatives or state when not to use it, but the department parameter and downstream-search promise make the context unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.