Skip to main content
Glama

article_extractor

Extract clean article content, metadata, and images from any news or blog URL. Returns one row per URL in Markdown, plain text, or HTML for RAG and LLM ingestion.

Instructions

Article & News Extractor returns clean article text, title, author(s), publish/modified date, tags and images from any news or blog URL — one row per URL, as Markdown, plain text or HTML. Billed to your own Apify account: ~$0.002 per result (Apify free-plan price, lower on paid plans).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesArticle URLs — Enter the article or blog post URLs to extract, one row is returned per URL, e.g. https://blog.apify.com/best-web-scraping-tools/. Works on news sites, blogs and any page that publishes a Schema.org Article/NewsArticle JSON-LD block, Open Graph tags, or a plain readable body. Example: ["https://blog.apify.com/best-web-scraping-tools/"].
outputFormatNoOutput format — Choose which body format(s) to include in each row. "Markdown" is the smallest and best for LLM/RAG ingestion; "all" returns markdown, text and html together for debugging or comparison. Options: markdown = Markdown; text = Plain text; html = HTML; all = All (markdown + text + html).markdown
includeImagesNoInclude images — Keep this on to return the mainImage and images fields and keep image references in the markdown/html body. Turn it off for a smaller, text-only dataset.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it discloses meaningful traits: one row per URL, selectable output formats, and the cost model ('~$0.002 per result', billed to the caller's own Apify account). It stops short of covering failure modes for unsupported pages, rate limits, or the auth/token requirement implied by 'your own Apify account'.

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?

Roughly two sentences: the capabilities/output list is front-loaded, followed by the pricing caveat. Dense but nearly waste-free; the dollar figure is arguably useful rather than filler.

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 3-parameter extraction tool with no output schema and no annotations, the description supplies the essential missing context: what fields come back, one-row-per-URL granularity, format options, and cost. It omits error/unsupported-page behavior and auth prerequisites, which keeps it from being fully complete.

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 reinforces that output is one row per URL and that Markdown/plain text/HTML are selectable, but adds no format syntax, enum nuance, or default behavior beyond what the schema already documents for url, outputFormat, and includeImages.

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 names a specific verb+resource ('Article & News Extractor returns clean article text, title, author(s), publish/modified date, tags and images from any news or blog URL') and enumerates the exact output fields, so the agent knows precisely what it produces. It does not, however, explicitly differentiate itself from siblings like website_to_markdown or structured_data_extractor, which it partly overlaps with, so it falls short of a 5.

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

Usage Guidelines3/5

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

Usage is only implied by the phrase 'from any news or blog URL' — there is no explicit statement of when to prefer this over website_to_markdown or structured_data_extractor, nor any when-not conditions. The billing note gives some practical context but is not routing guidance.

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