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optifeed

optifeed-radar

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shopping_check

Tests whether AI engines recommend specific products you supply. Returns each product scored 0-100, plus rival products that replace them.

Instructions

Check whether AI engines recommend specific PRODUCTS you name (beta). Returns each product scored 0-100 on whether engines recommend it, sorted with the ones they never named first, plus the rival products filling the shelf instead. Use for SKU-level questions; use check_visibility for the brand as a whole. There is no product discovery, so the list must be supplied. COST: spends real API money and is larger than a brand check. Each product costs about 4 prompts on every engine with a key, which extrapolates from measured check runs to roughly $0.20 per product across four engines; capped at $0.20 per product by default - pass max_cost to change it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesThe store site, e.g. example.com
enginesNoEngines to query; defaults to all with keys present
max_costNoHard cap on total spend in USD (default $0.20 per product)
productsYesUp to 10 products, in any order (the order is not a ranking; it only breaks ties between identical scores). Results are ordered by what the engines did: products they answered about but never recommended lead, then the rest by visibility, then anything the run could not measure. Each item is a name, or an object with name plus optional aliases and a descriptor ("quiet home espresso machine") that rescues an opaque product name.
Behavior4/5

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

No annotations, so description carries full burden. Discloses cost structure, default cap, that it's larger than brand check, and sorting behavior. Does not specify read-only status but implies it.

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?

Concise with no wasted words. Purpose is front-loaded, followed by usage guidance and cost. Each sentence serves a clear function.

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?

Covers purpose, usage, cost, limitation (no discovery), sorting, and return structure. Lacks explicit output field names, but describes return content adequately for agent understanding.

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 baseline 3. Description adds value by explaining ordering of products parameter, the role of aliases/descriptor, and cost implications for max_cost. Provides usage context beyond schema.

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?

Description uses specific verb 'Check' and resource 'AI engines recommend specific PRODUCTS.' It clearly distinguishes from sibling 'check_visibility' by stating 'Use for SKU-level questions; use check_visibility for the brand as a whole.'

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?

Explicitly states when to use (SKU-level questions) and when not (no product discovery, list must be supplied). Compares with sibling check_visibility. Does not mention other siblings but provides sufficient context.

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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