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ScrapeMole Search: web search, news search and autocomplete for AI agents: News search (articles)

news
Read-onlyIdempotent

Use for current events, monitoring a company/person/topic in the press, finding recent coverage before answering, or building news digests. Returns up to 20 articles with title, direct article url (no redirect wrapper), publisher, published time (ISO 8601), snippet and image when available. sort=date for newest first; freshness=hour|day|week|month to limit age; market for regional editions (en-US, en-GB, de-DE, …). Primary source is Bing News public RSS (retried automatically when Bing answers an empty page), fallback Brave Search News; the response names the source used. Empty results = 404, not charged. Paid tool: x402 USDC on Base, $0.002 per successful call; call without payment to receive the PaymentRequired object.

Input Schema

NameRequiredDescriptionDefault
qYesNews query (max 400 chars), e.g. a company, person, topic; site:reuters.com works on Bing.
pageNoResult page (1-5); each page is "count" results.1
sortNorelevance (default) or date (newest first).relevance
countNoNumber of results (1-20).10
marketNoMarket/locale as language-COUNTRY, e.g. en-US, en-GB, de-DE, fr-FR, ja-JP (applied on the Bing source).en-US
freshnessNoMaximum article age.any

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

With readOnly/openWorld/idempotent already declared, the description still adds substantial behavior: primary Bing News RSS with automatic retry on empty pages, Brave News fallback, source named in the response, 404-not-charged on empty results, and x402 USDC payment terms with a PaymentRequired object when unpaid. That is exactly the runtime context annotations cannot carry.

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?

Dense but front-loaded: usage triggers first, then return shape, then parameter hints, then source/error/payment details. Nearly every sentence carries load, though the long single-paragraph run-on structure slightly hurts scannability.

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?

For a 6-parameter, no-output-schema tool, the description covers return fields (title, direct URL, publisher, ISO timestamp, snippet, image), result limits, source and fallback behavior, empty-result handling, and billing. An agent has everything needed to call and interpret it correctly.

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 the baseline is 3; the description largely restates sort/freshness/market values already documented in the schema. It adds minor framing (market selects regional editions, freshness limits article age) but omits page/count and offers no new syntax beyond the schema.

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?

The description states a specific verb and resource — news search returning articles — and enumerates concrete use cases (current events, press monitoring, recent coverage, digests). The title frames it against web search and autocomplete, but the description itself never names the sibling tools, so differentiation relies on the title.

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 four clear triggers for when to use this tool (current events, monitoring, pre-answer coverage checks, digest building), which is strong positive guidance. It stops short of naming when NOT to use it or routing to the web/suggest siblings for non-article queries.

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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