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ForeverTools

Kiprio MCP

by ForeverTools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

readability

Extract article text and metadata from any web page, removing ads and clutter for clean reading or AI processing.

Instructions

Extract the main article text and metadata from a web page.

Uses Mozilla's Readability algorithm (the same engine as Firefox Reader Mode)
to strip navigation, ads, and boilerplate and return only the article content.
Ideal for summarising articles, building reading lists, or feeding clean text
into further AI processing without HTML noise.

Args:
    url: Full URL of the article or page to extract (must include https://).

Returns:
    title: Article or page title.
    byline: Author name(s) if detected.
    content: Plain text of the main article body.
    word_count: Number of words in the extracted content.
    published_at: Publication date if found in page metadata (may be null).
    excerpt: Short description/lead paragraph if available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

Tool Definition Quality

A4.7/5.0
Behavior4/5

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

Discloses algorithm source (Mozilla's Readability) and behavior (strips navigation/ads/boilerplate). No annotations exist, so description carries the burden. Lacks details on error handling (e.g., non-article pages, rate limits) but sufficient for typical use.

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?

Well-structured with two paragraphs: purpose/behavior then parameter/return details. Slightly verbose (e.g., 'Args' and 'Returns' sections repeat schema info), but each sentence adds value.

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?

Given single required parameter, no output schema, and no annotations, the description fully covers all necessary information: input format, return fields with descriptions, and expected behavior. No gaps identified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter (url) with schema coverage 0%. Description adds full semantics: 'Full URL of the article or page to extract (must include https://)'. This completely compensates for the missing schema descriptions.

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?

Clear verb 'Extract the main article text and metadata from a web page' with specific resource. Distinguishes itself from siblings (e.g., parse_og_tags, screenshot_url) by focusing on prose extraction.

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

Usage Guidelines5/5

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

Explicitly states ideal use cases: summarising articles, building reading lists, feeding into AI processing. No alternative tool is named, but the context signals show diverse sibling tools that clearly differ in purpose.

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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