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arstechnica_article

Fetch public Ars Technica article metadata and body paragraphs from a canonical URL. Uses a production browser fleet to overcome bot-verification challenges when standard HTTP requests fail.

Instructions

Get Ars Technica article content. Returns public Ars Technica article metadata and body paragraphs from a canonical article URL. The service uses the configured production browser fleet when ordinary HTTP transport encounters Ars Technica's bot-verification challenge; it does not bypass authentication or access controls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesCanonical Ars Technica article URL

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.5

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It discloses the fallback to a browser fleet for bot-verification and explicitly states it does not bypass authentication or access controls, which is valuable. It doesn't detail error behavior or response structure, but it does hint at the return content, offering reasonable transparency.

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 two sentences with no redundancy. The core purpose is front-loaded ('Get Ars Technica article content'), followed by clarifying scope and special handling. Every sentence earns its place, making it efficient and easy to parse.

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 a simple one-parameter fetch tool with no output schema, the description covers the essentials: what it returns (metadata and body paragraphs), the input requirement (canonical URL), and special behavior (bot-verification fallback). It could mention error handling or authentication limitations, but it already notes it only gets public content, so it's adequately complete for an agent to use 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 coverage is 100% because the single parameter 'url' is described as 'Canonical Ars Technica article URL'. The description reiterates this phrasing without adding extra syntax or format details. Since the schema already fully documents the parameter, the description adds minimal semantic value beyond what's already present.

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 the tool retrieves Ars Technica article content given a canonical URL, and specifies it returns metadata and body paragraphs. It differentiates from sibling tools like arstechnica_headlines (list-oriented) and arstechnica_author (author info) by focusing on a specific article.

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?

The description implies usage: when a specific article URL is known, use this tool. It doesn't explicitly mention alternatives, but the requirement of a 'canonical article URL' and the nature of sibling tools (headlines, news, sections) provide clear context. It also notes the bot-verification handling, which is a contextual hint.

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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