Skip to main content
Glama

Real Real Genuine

by io.github.richardjhobbs
OverviewDiscussionsAdmin

search_products

[FIND] START HERE when you know what you want. Free-text search across every active RRG listing. This endpoint answers DEFINED intent, not open browse. Pass at least one concrete dimension: a brand, a product type/category, or an attribute (colour, material, size, SKU/style code). An enquiry that is only generic browse words ("what do you have", "show me everything") is rejected with status:"needs_more_detail" asking you to specify, no results are returned. To browse without intent, call list_drops instead. Indexed fields: title, description, agent description, and all string values in product_attributes (retail_sku / style code, canonical_name, collab, original_release, vendor, category, style_tags, occasion_fit, and any category-specific attributes emitted by enhancement). Accepts any of these query patterns:

  • product name or partial name

  • SKU / style code / model number (exact or partial, dash/space insensitive)

  • brand name, or brand + category (" ")

  • collaborator name(s) for collab items

  • attribute keywords from the description ("black suede", "heavyweight cotton", etc.) Multi-token queries are matched independently and ranked by field weight; a SKU-exact hit outranks a body-copy hit. Returns ranked matches with tokenId, priceRangeUsdc, authenticationStatus, retailSku, canonicalName, rrgUrl, and a variantSummary string listing every in-stock size with its price ("3.5=$1583, 4=$1899, 10.5=$770, …").

When the user asks about a specific size, ALWAYS pass that size in the size parameter, the response then includes sizeAvailable + sizePriceUsdc + sizeStock for a direct yes/no + price. For queries like "size 10.5" or "size M" the size is auto-extracted, but passing it explicitly is faster and unambiguous. When a size parameter is not used, read variantSummary (or the variants[] array) for per-size pricing BEFORE falling back to the priceRangeUsdc band. Per-size prices are exact; the band is only a floor→ceiling range.

Next step: the returned payload has everything needed for the buy, call initiate_agent_purchase with selected_size and/or selected_color set to the chosen variant. Pass selected_color whenever the listing has a colour axis (variants[].color non-null) so fulfillment ships the right finish. get_drop_details is optional (adds signed image URLs + shipping context).

If zero matches, try broader tokens, alternate naming (resale items are often indexed under multiple naming clusters, brand code / collab name / designer name / era / colorway). If still zero, call list_drops to browse.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoOptional size filter (e.g. "10.5", "M", "UK 8"). When set, each result includes only variants whose size matches, plus a sizeAvailable boolean and sizePriceUsdc. Results with sizeAvailable=false are still returned (marked unavailable) so the agent can report correctly.
limitNoMax results (default 10)
queryYesFree-text query. Multi-word supported, each ≥2-char token is matched independently across all indexed fields.
brand_slugNoOptional brand slug to scope the search. Call list_brands to see slugs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / query / description
      Previous value: -"Free-text query. Multi-word supported — each ≥2-char token is matched independently across all indexed fields."New value: +"Free-text query. Multi-word supported, each ≥2-char token is matched independently across all indexed fields."
  2. Changed3 schema fields changed
    • changedInput schema / properties / brand_slug / description
      Previous value: -"Optional brand slug to scope the search (e.g. \"stadium-goods\")"New value: +"Optional brand slug to scope the search. Call list_brands to see slugs."
    • changedInput schema / properties / query / description
      Previous value: -"Free-text query. Multi-word is fine — each ≥2-char token is matched independently across all indexed fields. Examples: \"Jordan 1 Alaska 10.5\", \"AA3834-100\", \"Off-White Virgil Abloh\""New value: +"Free-text query. Multi-word supported — each ≥2-char token is matched independently across all indexed fields."
    • addedInput schema / properties / size
      Added value: +{
+  "description": "Optional size filter (e.g. \"10.5\", \"M\", \"UK 8\"). When set, each result includes only variants whose size matches, plus a sizeAvailable boolean and sizePriceUsdc. Results with sizeAvailable=false are still returned (marked unavailable) so the agent can report correctly.",
+  "type": "string"
+}
  3. Added

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries full burden and excels: it discloses rejection behavior (status 'needs_more_detail'), indexed fields, query pattern handling, ranking logic, size-specific response fields, pricing nuances (variantSummary vs priceRangeUsdc), and even zero-match fallback guidance. This goes far beyond the schema.

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 front-loaded with the most critical guidance ('START HERE'), and organized into clear paragraphs with headers-like patterns. Every section earns its place given the tool's complexity, though some redundancy (e.g., repeated notes about per-size pricing) could be trimmed.

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

Completeness5/5

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

For a complex tool with no output schema, the description covers all essential aspects: return fields, size semantics, pricing interpretation, next steps, and failure recovery. It also accounts for edge cases like unavailable sizes and multi-naming clusters, making it fully self-contained for an agent to act correctly.

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

Parameters5/5

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

Although schema coverage is 100%, the description adds substantial meaning beyond the schema: it explains query pattern semantics (SKU dash/space insensitivity, brand+category), auto-extraction of size, and how to use brand_slug (calling list_brands). It also clarifies the size parameter's effect on result structure, which is not obvious from the schema alone.

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 states a specific verb+resource ('Free-text search across every active RRG listing') and clearly differentiates from sibling tools by distinguishing defined intent from open browse. It also explicitly names list_drops as the alternative for browsing, making the tool's purpose unmistakable.

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

Usage Guidelines5/5

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

It provides explicit when-to-use guidance ('START HERE when you know what you want'), states when not to use it (generic browse words are rejected), and names alternatives ('To browse without intent, call list_drops instead'). It also gives concrete next-step instructions for purchases, which is highly actionable.

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

All Real Real Genuine toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources