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Glama

clean_web_content

Converts any raw web page into clean, LLM-ready markdown by removing ads, navigation, and noise.

Instructions

Scrapes and converts any raw web page into clean, LLM-ready markdown, eliminating ads, navbars, and noise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
auth_token_or_txNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.2.5

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that content is scraped, converted to markdown, and cleansed of ads/navbars/noise, which is useful. However, it does not mention authentication requirements, dynamic/JavaScript-rendered pages, rate limits, or failure behavior, especially given the unexplained auth_token_or_tx parameter.

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 a single efficient sentence with no filler. The action and output format are front-loaded, and the noise-removal detail earns its place by clarifying what 'clean' means.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value details do not need to be in the description. The main gap is the unexplained auth_token_or_tx parameter and the overbroad claim of 'any raw web page' when authenticated or dynamically rendered pages may behave differently. Still, for a straightforward public-page scraper, the description is mostly adequate.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, but it does not explain either parameter. The URL parameter can be inferred from the tool's purpose, but auth_token_or_tx is completely opaque—when it is needed, what format it should take, or what 'tx' means.

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 a specific action ('Scrapes and converts'), a target resource ('any raw web page'), and a concrete output ('clean, LLM-ready markdown'). It also differentiates from the sibling tools by specifying web pages rather than YouTube transcripts or PDFs.

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?

The intended use case is implied: use this for raw web pages, while clean_youtube_transcript and clean_pdf_research serve other sources. However, there is no explicit statement about when not to use it, nor any direct comparison to alternatives.

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