Fetch Variety article content from a canonical URL, returning metadata and body paragraphs as structured JSON for analysis or integration.
Get Variety article content. Returns public Variety article metadata and body paragraphs from a canonical article URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Canonical Variety article URL |
Changes observed during successful MCP inspections.
v1.17.5Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the operation is public, read-only in nature, and returns two specific things: metadata and body paragraphs. It does not mention rate limits or paywall behavior, but for a simple fetch-by-URL tool the transparency is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single well-structured sentence that front-loads the action and output, with no filler or redundant explanation. Every phrase adds functional information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one required parameter, fully described in the schema, and no output schema. The description explicitly states what is returned (metadata and body paragraphs), which is enough for an agent to invoke it correctly and know what to expect. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the parameter description ('Canonical Variety article URL') essentially mirrors the description text. The description adds no meaningful semantic detail beyond what the input schema already provides, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get'), names the resource ('Variety article content'), and qualifies it with 'canonical article URL', making it clearly distinct from sibling tools like variety_headlines, variety_news, and variety_sections. An agent can infer this tool is for retrieving a single full article rather than lists or metadata about sections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use the tool: when you have a canonical Variety article URL and need article content. It does not explicitly name alternatives or exclusion cases, but the 'canonical article URL' phrasing gives unambiguous context for this singular-article endpoint.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.