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Darshan972

Scrapingdog MCP Server

by Darshan972

Google News API

google_news

Scrape Google News headlines, sources, and timestamps by search query, topic, or publication. Retrieve structured data for news monitoring and analysis.

Instructions

Scrape Google News headlines, sources and timestamps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
soNoSort order (only with story_token): 0 = relevance, 1 = date. (API default: 0)
queryNoSearch terms. Supports operators like site: and when:. Cannot combine with *_token params.
countryNoTwo-letter ISO country code to geo-target results (e.g. us, gb, in, de). (API default: us)
languageNoResult language code (e.g. en, es, fr, de). (API default: en)
topic_tokenNoTopic identifier (World, Business, Technology, ...). Mutually exclusive with query.
section_tokenNoSubsection token; only with topic_token or publication_token.
publication_tokenNoPublisher identifier. Mutually exclusive with query.
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It only mentions output components, not pagination, rate limits, limitations, or whether it returns full content. The term 'scrape' implies unofficial access but no specifics are provided.

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?

One sentence, immediately front-loaded with the action and result. No filler words, fully efficient.

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?

The schema richly documents 7 parameters, and the description conveys core purpose and return values. However, it does not introduce the query vs. token modes or geo/language options, which are discoverable via schema but would benefit from a brief mention. Still, adequate given schema support.

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 all 7 parameters are documented in the schema. The description itself adds no parameter-level meaning beyond mentioning output types, which is not parameter semantics. 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 uses a specific verb 'scrape' with resource 'Google News' and lists return types (headlines, sources, timestamps), clearly differentiating from siblings like google_search and google_trends.

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?

No guidance on when to use this tool versus alternatives. The description only states the action without exclusions or comparisons to sibling tools like google_search or google_ai_overview.

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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