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Article Content Extractor for AI Agents

industrial_platform/article-content-intelligence

industrial_platform--article-content-intelligence
Destructive

This tool calls the Actor "industrial_platform/article-content-intelligence" and retrieves its output results. Actor description: Extract clean article text and metadata. Price: $0.002 per successful article.

This tool requires an x402 payment. Include a valid x402 payment signature in the request metadata (_meta["x402/payment"]). Your MCP client must support the x402 payment protocol.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYes**REQUIRED** Public HTTP/HTTPS article pages to inspect. Up to 100 unique URLs per run. Example values: ["https://www.iana.org/help/example-domains"]
waitSecsNoMax seconds (0–45, default 30) to cap the wait for the Actor run to reach terminal state. For long-running Actors the response returns at the cap with the current run status; follow `nextStep` to poll via get-actor-run. Set to 0 to fire-and-forget.
concurrencyNoMaximum number of URLs fetched concurrently during this run. Example values: 10
max_text_charsNoMaximum extracted article text retained per successful URL. Example values: 100000
timeout_secondsNoMaximum time allowed for each individual page fetch. Example values: 30

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=true and openWorldHint=true, so the safety profile is covered structurally. The description adds genuinely non-redundant behavior: a per-article price ($0.002 per successful article), the requirement to embed a valid x402 payment signature in _meta["x402/payment"], and the constraint that the MCP client must support the x402 protocol. No return-format or rate-limit detail beyond that, so not a 5.

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?

Three short sentences, front-loaded with what the tool does before moving to price and payment mechanics. The framing sentence ("calls the Actor ... and retrieves its output results") is slightly boilerplate for this MCP server, but nothing is wasteful or buried.

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?

An output schema exists, so return values need not be explained, and the description covers the one thing the structured fields cannot: the paid x402 invocation requirement. Run semantics (fire-and-forget, polling) live in the waitSecs schema entry rather than the description, which is acceptable given it is documented there.

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% and all five parameters carry their own descriptions (URL limits, waitSecs polling semantics, concurrency, text cap, per-URL timeout), so the schema does the heavy lifting. The description contributes nothing about parameter meaning, which is the correct baseline-3 outcome when coverage is complete.

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 states a specific verb+resource: it calls the Actor "industrial_platform/article-content-intelligence" and retrieves its output, and the embedded actor description clarifies the actual job is extracting clean article text and metadata from URLs. This is far clearer than a tautology and an agent can tell roughly what it produces. It does not, however, explicitly differentiate itself from siblings like get-dataset-items, 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 context is implied (fetch and analyze article pages) and the description surfaces the x402 payment precondition, which is real invocation guidance. But there is no explicit when-to-use vs when-not, and no direction on choosing it over get-dataset-items or get-actor-run for the same Actor's data.

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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