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Scrapiq

Extract a URL to clean content

scrapiq_extract

Extract a web page into clean, structured content for LLM/RAG pipelines via the Scrapiq API. Strips boilerplate, navigation, ads, and scripts. Returns title, content, links, and metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL to extract
formatNoOutput format (default: markdown)
max_charsNoTruncate content to N chars (default: no truncation)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses key behaviors (stripping boilerplate, returning structured fields) but omits details like authentication requirements, rate limits, or error handling. It also doesn't clarify that the operation is read-only, though that's implied. The description is honest but not exhaustive.

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 long, with the primary action and purpose in the first sentence. It is front-loaded with the core functionality and avoids unnecessary elaboration. Every word contributes to the tool's understanding.

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?

The description covers the essential information an agent needs: purpose, behavior, and return values. Since there's no output schema, it explicitly lists the returned fields. It lacks details on authentication, potential failures, or rate limits, but for a straightforward extraction tool with fully documented parameters, this is sufficient.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds no additional parameter semantics beyond what the schema already states (e.g., format, max_chars). It doesn't clarify defaults or edge cases, but that's adequately handled by the schema.

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's action (extract a web page), the resource (URL), and the intended use case (LLM/RAG pipelines). It also mentions what it removes (boilerplate, navigation, ads, scripts) and what it returns (title, content, links, metadata), making it unambiguous.

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 provides context on when to use the tool (for LLM/RAG pipelines needing clean content) but doesn't explicitly state when not to use it or mention alternatives. Since there are no sibling tools, the usage context is clear enough; however, it lacks explicit exclusions or prerequisites.

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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