Fetch Live Science article metadata and body paragraphs from a canonical URL. Use it to extract public article content for research, summaries, or analysis.
Get Live Science article content. Returns public Live Science article metadata and body paragraphs from a canonical article URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Canonical Live Science article URL |
Changes observed during successful MCP inspections.
v1.17.5Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It adds useful context by noting the content is 'public' and that input must be a 'canonical article URL', and it states the return shape ('metadata and body paragraphs'). However, it does not disclose edge-case behavior, error handling, URL format requirements, or whether the operation is strictly read-only beyond the word 'Get'.
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?
The description is one tightly scoped sentence that front-loads the primary action and resource, then adds the return details. There is no filler, repetition, or extraneous 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?
For a simple single-parameter, no-output-schema fetch tool, the description covers the essential facts: what it retrieves, from where, and what the caller gets back. It is not exhaustive about failure modes or strict URL formats, but given the low complexity, nothing critical is missing for an agent to invoke it correctly.
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 description coverage is 100%, and the single 'url' parameter is already described as 'Canonical Live Science article URL'. The description repeats the canonical-URL concept but adds no new parameter-level meaning, so it meets the baseline without exceeding it.
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?
Description states a specific verb ('Get'), a precise resource ('Live Science article content'), and details what is returned ('metadata and body paragraphs'). It also scopes the input to 'a canonical article URL', which distinguishes it clearly from sibling tools like livescience_headlines or livescience_sections that operate without a specific article URL.
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 implies usage: use this tool when you have a canonical Live Science article URL and want its content. However, it does not explicitly contrast with alternative tools, mention when not to use it, or refer to sibling tools like livescience_author or livescience_news, so the guidance relies on inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.