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discover_products

Search products ACROSS every merchant in the Facet network that has opted into cross-merchant discovery. Returns matches each carrying the selling merchant's terminal_url (point your catalog and checkout calls there). Filter by free-text query (product name + description), exact category, and/or tags (all must be present); at least one filter is required. Use this when you know WHAT you want but not WHICH merchant sells it. REQUIRES IDENTITY: send a Facet KYA as Authorization: Bearer <kya> on the MCP request. A KYA is an ES256 JWT from Facet's default issuer https://issuer.facet.llc (or another issuer this Terminal trusts; see KYA-Issuers in its /.well-known/agents.txt). Calling without one returns 401 with a signup link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTags that must ALL be present on a matching product.
limitNoMax matches to return. Default 10, cap 50.
queryNoFree-text over product name + description, e.g. "lavender soap".
categoryNoExact product category to match.

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the identity requirement (KYA token), the 401 failure mode, the cross-merchant opt-in scope, and the terminal_url return behavior. This is substantial, though it does not mention pagination, sorting, or empty-result behavior.

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?

The description is front-loaded with the core purpose, then parameter semantics, then usage context, then auth details. Every sentence earns its place, and the structure uses clear signposting (e.g., 'REQUIRES IDENTITY:') without fluff.

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 moderately complex search tool with no output schema and no annotations, the description covers what it returns (matches with terminal_url), how to filter properly, when to use it, and the authentication prerequisite. It even advises on next steps (point catalog/checkout calls at terminal_url). This is sufficient for an agent to use the tool 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?

The schema already covers all parameters at 100% coverage, so the baseline is 3. The description adds meaningful constraint information: 'at least one filter is required' and clarifies the relationship between filters ('and/or', 'all must be present'). This goes beyond the schema without fully duplicating it.

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 opens with a specific verb+resource+scope: 'Search products ACROSS every merchant in the Facet network that has opted into cross-merchant discovery.' This clearly distinguishes it from sibling tools like discover_businesses (businesses vs products) and search_catalog/lookup_catalog (likely merchant-scoped).

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?

It provides an explicit usage condition: 'Use this when you know WHAT you want but not WHICH merchant sells it.' This gives clear context. However, it does not name specific alternatives or explicitly state when not to use it, so it stops short of a 5.

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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TDQS

A3.6/5.0
Disambiguation3/5

Most tools map to a distinct lifecycle step, and the detailed descriptions separate discovery, cart, checkout, payment, order, and wishlist concerns well. However, get_product and lookup_catalog are largely the same lookup by identifier, discover_products and search_catalog overlap across scope, and update_checkout is actually a read-only no-op.

Naming Consistency4/5

The set overwhelmingly follows a verb_noun pattern: create_cart, get_checkout, cancel_checkout, complete_checkout, search_catalog. Minor deviations are the wishlist_* resource-first subgroup and the misleading update_checkout, which does not perform an update.

Tool Count3/5

At 23 tools, this sits at the heavy end of the range, though the broad commerce lifecycle does justify substantial surface area. The count is inflated by redundant product lookup tools and a no-op update_checkout, so a more trimmed set in the low-to-mid teens would be healthier.

Completeness4/5

The server covers the full shopper lifecycle well: discovery, product lookup, cart, checkout, payment, order placement, order read, refunds, and wishlist. Gaps are minor and workable, such as update_checkout not actually supporting updates and no explicit order-cancellation path once a checkout is complete.

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