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webscraping-ai

WebScraping-AI MCP Server

Official

Search Google

webscraping_ai_serp
Read-only

Search Google and return parsed JSON results with organic results, related searches, search information, and pagination to find pages for scraping.

Instructions

Search Google and get parsed results as JSON: organic_results (position, title, link, domain, displayed_link, snippet, date), related_searches, search_information (including spelling corrections) and pagination. 10 results per page; position restarts at 1 on every page. Priced per search (see https://webscraping.ai/docs#serp); failed searches are not charged. Use it to find pages, then read them with webscraping_ai_text or the other tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch query.
glNoTwo-letter country code for the search, e.g. us, gb, de (us by default).
hlNoTwo-letter language code for the results, e.g. en, de, fr (en by default).
pageNoResults page number, 10 results per page (1 by default).
engineNoSearch engine to query (google by default).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.2.2

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, non-destructive, openWorld). The description adds real behavioral context beyond them: 10 results per page, position restarting at 1 on each page, per-search pricing, and the fact that failed searches are not charged. It does not describe rate limits or result freshness.

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 tight sentences: the return shape is front-loaded, then pagination/pricing constraints, then the routing hint. Every clause carries information an agent needs, with no filler or restated tool name.

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?

With no output schema, the description takes on the burden of describing the return payload and does so by enumerating the top-level fields and key sub-fields, plus pagination and cost behavior. Nothing needed to call this tool correctly appears to be missing.

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 the baseline is 3, but the description adds semantics the schema does not carry — notably the 10-results-per-page size and the position restart behavior that governs how the 'page' parameter should be used. That is meaningful added value over the schema's bare 'Results page number' text.

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 states a specific verb+resource ('Search Google') and names the exact return payload (organic_results with position, title, link, snippet, etc.), which no sibling tool produces. An agent can distinguish it from webscraping_ai_text or webscraping_ai_html without opening any 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?

It gives explicit usage context — 'Use it to find pages, then read them with webscraping_ai_text or the other tools' — which routes the agent to a specific downstream sibling. It stops short of stating when NOT to use it or which sibling to prefer for a direct-page read, so it is clear but not exhaustive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.