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Zenserp MCP by usefulapi

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zenserp_image_search
Destructive

Google image results (thumbnails, source pages, sizes). SPENDS 1 request of the monthly quota per successful call (failed calls are not charged); do not repeat a call needlessly. Each page holds 100 images. GET /v2/search (tbm=isch).

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.
ijnNoImage page number, 100 images per page; per the docs, images 101-200 = ijn 2. Omit for the first page.
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

A3.7/5.0
Behavior4/5

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

Adds genuinely useful behavior beyond the annotations: exactly 1 quota unit charged per successful call, failed calls free, 100 images per page, and the underlying endpoint (GET /v2/search, tbm=isch). This quota/cost disclosure is the key behavioral fact for a non-idempotent, costly endpoint and pairs sensibly with destructiveHint=true.

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?

Three tight sentences, front-loaded with the resource and payoff, followed by the cost warning and endpoint. Every sentence earns its place, though the endpoint string is somewhat implementation-detail-heavy for a top-level description.

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?

For an 11-parameter tool with no output schema, the description covers return content, page sizing, and cost, which is most of what an agent needs. Pagination mechanics and location/device effects are left to the schema, acceptable given 100% coverage.

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% and each parameter description is rich (including cross-references to zenserp_list_countries/languages), so the schema carries the semantics. The description only reinforces page size ('Each page holds 100 images'), adding marginal value over the ijn/start docs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Google image results') and enumerates the payload (thumbnails, source pages, sizes), so an agent knows exactly what this returns. It does not, however, distinguish itself from sibling zenserp_reverse_image_search or zenserp_video_search, leaving that inference to the name.

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

Usage Guidelines3/5

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

Gives cost-driven usage guidance ('do not repeat a call needlessly') and implies the tool's place in a quota-limited flow, but never states when to prefer it over siblings like reverse_image_search or when-not to use it. Usage is implied rather than spelled out.

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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