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extract_content

Read-only

Retrieve structured content from any web URL as markdown, text, HTML, article, links, or metadata for AI agents and data workflows.

Instructions

Extract content from a web page. Returns structured data based on the extraction type. Supports: markdown (readable content), text (plain text), html (raw HTML), article (structured with title/author/excerpt), links (all page links), metadata (OG tags, title, description).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to extract content from (must start with http:// or https://)
typeNoExtraction type (default: markdown)
cacheNoUse cached result if available (default: false)
delayNoMilliseconds to wait after page load (default: 0)
selectorNoCSS selector to scope extraction to a specific element
block_adsNoBlock advertisements and trackers (default: true)
cache_ttlNoCache TTL in seconds (default: 86400)
max_lengthNoMaximum content length in characters (default: 100000)
block_cookie_bannersNoRemove cookie consent banners (default: true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.6

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety is covered. The description adds real value beyond that by disclosing what each extraction type returns (article = title/author/excerpt, metadata = OG tags) in the absence of an output schema. It still omits rate limits, failure modes, and JS-rendering behavior.

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?

Three tight sentences, front-loaded with the core action and result, followed by a compact mode list. No filler or repetition.

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?

With no output schema, the description does the necessary work of sketching return formats per mode, and 100% schema coverage handles the 9 parameters. It lacks guidance on dynamic pages, error cases, or the relationship between cache/cache_ttl/delay, which would make it fully complete for a scraping tool.

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 defaults, ranges, and semantics for all 9 parameters are already documented in the schema. The description echoes the type enum values but adds no syntax or behavior detail beyond them, making the baseline 3 appropriate.

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?

States a specific verb and resource ('Extract content from a web page') and immediately clarifies the return shape ('structured data based on the extraction type'). The enumeration of the six extraction modes makes its scope unambiguous and clearly separates it from the screenshot/webhook siblings.

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 list of extraction types implicitly guides the agent toward the right mode, but there is no explicit when-to-use guidance, no exclusions, and no mention of alternatives such as take_screenshot for visual capture. Usage must be inferred from the mode enumeration.

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