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torontostar_article

Fetch public Toronto Star article metadata and body paragraphs from a canonical URL. Returns only teaser text when articles are metered or paywalled.

Instructions

Get Toronto Star article content. Returns public Toronto Star article metadata and the body paragraphs shown to an anonymous reader from a canonical article URL. The Star meters its articles, so paywalled is true and paragraphs holds only the public teaser (typically the first two paragraphs) whenever the page hides the rest from non-subscribers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesCanonical Toronto Star article URL

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.5

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It transparently explains the paywall behavior: that 'paywalled' field indicates if the article is metered, and 'paragraphs' only contains the public teaser (first two paragraphs) for non-subscribers. This crucial detail about partial content is disclosed, along with the fact that it fetches content shown to an anonymous reader, which is a clear behavioral trait.

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 and front-loaded with the purpose. It comprises three sentences: a clear imperative, a statement of what it returns, and a behavioral note. There is no unnecessary verbosity, and the critical paywall detail is included without bloat. It is well-structured and efficient.

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?

Given the tool's simplicity (one parameter, no output schema), the description adequately covers what the tool returns (metadata and paragraphs) and the special paywall condition. It explains the 'paywalled' and 'paragraphs' fields, which gives an idea of the output shape. It does not list all metadata fields or error cases, but for a basic article fetcher, 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?

Schema description coverage is 100%, so the parameter 'url' is already documented as 'Canonical Toronto Star article URL'. The description repeats this without adding any additional meaning, such as format requirements, examples, or constraints. It does not go beyond what the schema 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.

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 purpose: to get Toronto Star article content. It specifies the verb 'Get', the resource 'Toronto Star article content', and explicitly mentions it returns metadata and body paragraphs from a canonical article URL. This distinguishes it from sibling tools like torontostar_author (author info) or torontostar_headlines (listings).

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 implies when to use this tool (when you need article content from a specific URL) but does not explicitly contrast it with alternatives like torontostar_headlines or torontostar_news. It provides clear context for the intended use but lacks explicit exclusions or guidance on when to choose a different tool.

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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