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Zenserp MCP by usefulapi

Search Google Shopping

zenserp_shopping_search
Destructive

Google Shopping listings for a query: product, price, merchant, rating, and the product_id for zenserp_shopping_product. SPENDS 1 request of the monthly quota per successful call (failed calls are not charged); do not repeat a call needlessly. GET /v2/search (tbm=shop).

Input Schema

NameRequiredDescriptionDefault
qYesThe query.
glNoCountry (gl), two letters, e.g. us, de, gb. zenserp_list_countries lists the supported codes. Default: United States.
hlNoInterface language (hl), e.g. en, de, pt-BR. zenserp_list_languages lists the supported codes.
lrNoLimit results to these languages (lr), prefixed with lang_ and joined by |, e.g. lang_fr.
tbsNoGoogle 'tbs' filter string for what the query field cannot express, e.g. qdr:w (past week).
cacheNotrue = serve a stored copy when one exists (kept 24 h, NOT charged) and store the reply when not. Default false: every call is fetched live and charged.
startNoResult offset: 0 (default) first page, 10 second page, 20 third page ... Google serves about 300 results per query.
deviceNoDevice to emulate.
fieldsNoOnly return these top-level keys of the reply (for example organic, paid_results, related_searches). Saves context; the call is charged the same.
locationNoGeolocation of the search, in Google's canonical form, e.g. 'Manhattan,New York,United States' (see zenserp_search_locations). Set it when the result depends on place, especially ads.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The annotations mark this destructive (destructiveHint=true), which is unusual for a read-like search, and the description explains the basis: it 'SPENDS 1 request of the monthly quota per successful call' and failed calls are not charged. That quota/cost disclosure adds meaning beyond the structured hints. It does not discuss rate-limit behavior or the 24h cache interaction, so it stops at a 4.

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?

Three compact sentences, front-loaded with the resource and return contents, then cost, then the underlying endpoint. Every sentence carries information an agent needs and there is no filler.

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 no output schema, the description usefully enumerates returned fields (product, price, merchant, rating, product_id) so the agent knows what comes back. Combined with the quota disclosure and a fully-covered input schema, it is close to complete; only pagination/result-count expectations and cache behavior are left to the schema.

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 description coverage is 100%, so every parameter is already documented in the schema. The description adds no parameter-level syntax or defaults beyond what is there (the listed items are return contents, not inputs). Baseline 3 applies when the schema does the heavy lifting.

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?

States a specific verb and resource ('Google Shopping listings for a query') and enumerates what each listing contains (product, price, merchant, rating, product_id). It also names the sibling zenserp_shopping_product that consumes the product_id, so an agent can distinguish this from zenserp_search and other vertical searches without opening a schema.

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?

Gives clear context for use (Shopping listings) and an explicit follow-on path via the product_id for zenserp_shopping_product, plus a usage caution ('do not repeat a call needlessly'). It stops short of naming an exclusion — e.g. when to prefer zenserp_search with tbm=shop or a different vertical — so it is clear but not fully routing.

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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