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Crawlora MCP

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by Crawlora-org
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JavaScript
Remote

cnn_article

Extract structured CNN article data from a canonical cnn.com URL, including headline, author, publication and update times, section, image, and body paragraphs.

Instructions

CNN article content. Returns a CNN article's headline, description, author, publication and update times, section, image, and body paragraphs. Provide a canonical cnn.com article URL.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "cnn_article"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesCanonical cnn.com article URL

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It lists returned fields but does not mention potential errors, rate limits, or other side effects. The instruction to provide a canonical URL is a usage hint rather than behavioral transparency. It is adequate but not rich.

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?

The description is concise—two sentences, front-loaded with the resource and returned fields. It is not verbose, though the opening 'CNN article content' is somewhat redundant given the tool name.

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?

For a simple one-parameter fetch tool, the description enumerates all returned components, which is sufficient even without an output schema. It does not mention edge cases (e.g., article not found) but is otherwise complete for its complexity.

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% (the url parameter has a description). The description's 'Provide a canonical cnn.com article URL' mirrors the schema, adding no extra meaning about format or constraints beyond what the schema already states.

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 returns a CNN article's headline, description, author, publication/update times, section, image, and body paragraphs. It is specific and distinguishes itself from siblings like cnn_headlines (likely headline-only) and cnn_live_story.

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 description implies usage (fetch full article content) but does not explicitly state when to use this tool versus cnn_headlines or cnn_live_story. No exclusions or alternative pointers are given; the context is implicit rather than explicit.

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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