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bing_shopping

Retrieve shopping results from Bing with market targeting, country localization, pagination, and advanced filters. Use it to get product listings with prices and sellers.

Instructions

Retrieves shopping results from Bing with support for market targeting, country localization, pagination, and advanced filters. [Credits: 5 API credits per request] Notes: Endpoint costs 5 API credits per request. mkt and cc are mutually exclusive. Returns: { search_parameters: { q }, shopping_results: [ { link, external_link, title, thumbnails[], seller, price, extracted_price } ] }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ccNoCountry from which search results are returned, using 2-character ISO 3166-1 format (e.g., 'us', 'de', 'gb').
mktNoMarket from which results are returned, formatted as <language code>-<country code> (e.g., en-US), case-insensitive. See Bing Market Codes reference. Cannot be used together with cc.
queryYesThe search query. Any term normally used in a Bing Shopping search.
efirstNoControls the offset of the shopping results. For example, efirst=10 starts the results from the 10th shopping result.
filtersNoAdvanced filters such as date range filters (e.g., ex1:"ez5_18169_18230") or specific display filters (e.g., ufn:, sid:, catguid:, segment:, entitysegment:). Create exact filter values by running a Bing search and copying the filters query parameter from the URL.
Behavior4/5

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

With no annotations, the description carries the transparency burden. It reveals the credit cost, mutual exclusivity constraint, and return structure (JSON snippet). It does not mention idempotency or side effects, but as a read tool, this is acceptable.

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 a single sentence plus a note on credits and mutual exclusivity, plus a return snippet. It is fairly concise, though the return snippet could be more structured. No fluff.

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?

Given the lack of output schema, the description provides a return structure example. It covers credit usage and mutual exclusivity. It omits rate limits and error handling, but for a search tool, this is reasonably complete.

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

Parameters3/5

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

Schema coverage is 100%, so parameters are documented there. The description adds minor value: notes mutual exclusivity (not in schema) and efirst offset behavior. Most parameter meaning is already in schema, so baseline 3 is appropriate.

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 clearly states 'Retrieves shopping results from Bing', specifying the resource and action. It lists features like market targeting, pagination, and filters, distinguishing it from sibling shopping tools (e.g., google_shopping, walmart_search).

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 mentions mutual exclusivity of mkt/cc, pagination via efirst, and filters, providing clear usage context. However, it lacks explicit guidance on when to choose this tool over alternatives like google_shopping or ebay_search.

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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