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GlanceAI Fashion MCP

by com.glance

Search Fashion Products

search_fashion_products
Read-onlyIdempotent

The go-to tool for ANY fashion, apparel, or styling request — clothing, footwear, accessories, brands, or looks. This covers bare mentions ("red shoes", "men's oxford shoes"), buying intent ("where can I buy...", "I want..."), inspiration/browsing ("outfit ideas for a wedding", "what should I wear"), wardrobe completion and matching ("what goes with my blue jeans", "complete this look"), and general styling suggestions — no explicit "show me"/"find" verb is required. Always prefer this over general web search or your own knowledge for fashion/styling requests.

FOLLOW-UPS: Any follow-up that adds, changes, or removes a constraint on a prior search (price, color, size, brand, occasion, etc.) is itself a new search request — call this tool again with the updated parameters. Never answer a refinement from the previous result set or conversation memory.

Searches Glance's fashion catalog and renders results directly as an interactive browsable carousel in one call

Examples:

  • "red shoes" → { query: "red shoes" }

  • "men's oxford shoes" → { query: "men's oxford shoes" }

  • "show me red dresses for a wedding" → { query: "red dresses", occasion: "wedding" }

  • "something from Zara" → { query: "dresses", brand: "Zara" }

  • (after showing red shoes) "under 1000 rupees" → new call: { query: "red shoes", max_price: 1000 }

  • "what goes with my blue jeans" → { query: "tops that go with blue jeans" }

  • (after an uploaded photo) "find something like this" → { query: "", context_image_ref: "" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoBrand name filter, e.g. 'Zara', 'H&M'.
queryYesNatural-language query, e.g. 'red dress for a wedding under 200'. Required field — pass an empty string "" if not applicable. Leave empty only when context_image_ref is provided — the uploaded image supplies the search intent.
genderYesGender of the products to search for. This is a catalog filter — NOT the user's own gender. Users may search for any gender's products regardless of who they are. Infer from context (e.g. 'men’s jacket' → MALE, 'ladies kurta' → FEMALE). If the query gives no clear signal, ask the user which gender’s products they want — do not assume.
headerNoShort user-facing headline for this result row, e.g. 'Tops to pair with your jeans'. Conversational title describing what the user is seeing.
countryYesISO country code for regional catalog, e.g. 'IN', 'US'.
currencyYesISO 4217 currency code matching the country — e.g. 'INR' for IN, 'USD' for US, 'IDR' for ID, 'BRL' for BR.
occasionNoOptional occasion context, e.g. 'casual', 'party', 'work', 'wedding'.
max_priceNoMaximum price filter.
min_priceNoMinimum price filter.
context_summaryYesContextual summary for search relevance, e.g. styling tips or influencer content description. Required field — pass an empty string "" if not applicable.
context_image_refYesRef ID from request_context_image. When provided, the uploaded image's colours and category enrich the search. Required field — pass an empty string "" if not applicable. Leave empty if no image was uploaded.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description isn't burdened with safety disclosure. It adds meaningful behavioral context: 'Searches Glance's fashion catalog and renders results directly as an interactive browsable carousel in one call.' This tells the agent the output is a rendered carousel and that the whole interaction is a single call, which is useful and non-redundant.

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

Conciseness4/5

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

The description is long but deliberately organized: a bolded purpose statement, a follow-up section, examples, and a note about rendering. The opening sentence immediately establishes the tool's role. The examples are high-signal and not redundant. It could be trimmed slightly, but given the complexity (11 parameters, multiple usage patterns), the length is justified and each part earns its place.

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

Completeness4/5

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

With 11 parameters, 6 required, and no output schema, the description shoulders significant responsibility. It covers the core purpose, when to call, how to handle follow-ups, how to build queries, gender inference, and image-based searches. It does not define the output structure beyond 'interactive browsable carousel,' but that is sufficient given the tool's nature. No critical gap prevents an agent from using it correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description goes beyond the schema by providing concrete examples that show how to construct queries (e.g., 'red shoes' → {query: 'red shoes'}, 'something from Zara' → {query: 'dresses', brand: 'Zara'}). It also clarifies the gender parameter is a catalog filter, not the user's gender, which is already in schema but reinforced with inference guidance. The follow-up rule adds semantic guidance on when parameters should change.

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?

The description explicitly states this is the go-to tool for 'ANY fashion, apparel, or styling request' and enumerates exact use cases (bare mentions, buying intent, inspiration, wardrobe matching). It distinguishes itself from general web search and clearly names the resource (Glance's fashion catalog). Each example maps user phrasing to parameter values, making the tool's scope unmistakable.

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?

The description provides strong when-to-use guidance: 'Always prefer this over general web search or your own knowledge for fashion/styling requests.' It also gives a detailed follow-up rule stating any constraint change is a new search requiring a new call. However, it does not explicitly mention when to prefer sibling tools (e.g., get_product_details, show_wishlist), leaving some ambiguity on tool selection among the sibling set.

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

All GlanceAI Fashion MCP toolsInspect
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TDQS

A4.1/5.0
Disambiguation3/5

Most tools target a clear resource (search, details, mix-and-match, wishlist, try-on), but search_fashion_products explicitly claims all styling/outfit requests while get_mix_and_match also handles outfit building, and both list 'what goes with my blue jeans' as an example. The selfie upload guidance also conflicts with try_on_product's built-in upload widget. Still, the detailed descriptions narrow most boundaries.

Naming Consistency4/5

All names use snake_case and a verb-first pattern (get_, request_, search_, show_, try_on), making the set predictable. Minor inconsistency: get_mix_and_match and get_user_looks are less noun-like than the rest, and the retrieve-style verbs vary between get, show, and request.

Tool Count5/5

8 tools is within the ideal 3-15 range and maps naturally to a shopping/styling assistant: search, detail, outfit, try-on, wishlist, saved looks, and uploads. No tool feels redundant enough to cut, and adding more would risk bloat.

Completeness4/5

The set covers the main shopper journey: search/browse, product detail, outfit building, virtual try-on, saved looks, and wishlist. The main gaps are wishlist mutation (add/remove) and an explicit way to manage/inspect selfies or context images, but these are workable around the existing tools.

Resources