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scrapeunblocker-mcp-remote

Google search results

google_search
Read-only

Run a Google search through the ScrapeUnblocker API (https://developers.scrapeunblocker.com) and return organic results as JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordYesThe search query.
proxy_countryNoOptional ISO country code to search from, e.g. 'US'.
pages_to_checkNoHow many result pages to collect (default 1).

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the description does not need to emphasize that it is a read operation. However, it adds value by specifying the external API and that results are limited to organic results in JSON format. It does not disclose potential rate limits, costs, or behavior when results are limited.

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?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and output format without extraneous words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three parameters, no output schema, and no nested objects, the description is minimally complete. It covers the core function but lacks detail on the JSON structure or any potential differences in returned data. It is adequate but not rich.

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%, meaning all parameters are described in the input schema. The description adds no additional meaning beyond what the schema provides. The baseline is 3.

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 that the tool runs a Google search via a specific API and returns organic results as JSON. It uses a precise verb ('Run a Google search') and identifies the resource and output format, distinguishing it from sibling tools like fetch_html and fetch_parsed which retrieve web pages directly.

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

Usage Guidelines2/5

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

The description offers no guidance on when to use this tool versus alternatives. It does not mention criteria for choosing google_search over fetch_html or fetch_parsed, nor does it specify any prerequisites or limitations.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool maps to a distinct output type: rendered HTML, parsed JSON, search results, or element selectors. fetch_html and fetch_parsed both fetch a page but explicitly serve different extraction needs, so an agent can reliably tell them apart.

Naming Consistency4/5

All names use lowercase snake_case and mostly follow an action_target pattern: fetch_html, fetch_parsed, list_elements. google_search breaks the pattern slightly but is still predictable and readable.

Tool Count5/5

Four tools is a compact but well-scoped set for a scraping API wrapper. Each tool exposes a distinct capability and none is redundant.

Completeness5/5

The set covers the core scraping lifecycle: rendering HTML, getting structured data, searching Google, and discovering selectors for interaction. Combined with the steps parameter on fetch_html, agents can navigate, interact, and extract without major gaps.