Retrieves all 57 editorial categories in The National's section taxonomy accepted by national_headlines, excluding podcast, newsletter, puzzle, partner, and promotional surfaces.
Get The National sections. Returns all 57 editorial categories in The National's section taxonomy that are accepted by national_headlines. Podcast, newsletter, puzzle, partner and promotional surfaces are excluded.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.17.9Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden and it does add real behavioral context: the exact count returned (57) and the explicit list of excluded surfaces (podcast, newsletter, puzzle, partner, promotional). It does not mention pagination, caching, or freshness, but for a static taxonomy listing the exclusions are a meaningful, useful trait.
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?
Two sentences, front-loaded with what the tool returns, followed immediately by the exclusion scope. No filler, no repetition of the name, and every clause carries 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 zero-parameter listing tool with no output schema, the description is nearly sufficient: it states what is returned, how many items, and what is filtered out. It could note the return shape (e.g., category ids vs. display names) which matters for feeding national_headlines, but overall an agent has what it needs.
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?
The tool takes zero parameters, so the schema-based baseline of 4 applies. There is nothing for the description to clarify about arguments, and it correctly adds no spurious parameter guidance.
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?
States a specific verb and resource ('Get The National sections') and quantifies the return ('all 57 editorial categories in The National's section taxonomy'). It distinguishes itself from siblings by naming national_headlines as the consumer of these categories.
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 phrase 'accepted by national_headlines' implies these values are meant as inputs to national_headlines, giving some usage context. However, there is no explicit when-to-use statement, no comparison against national_news or national_article, and no explanation of why an agent would fetch sections rather than headlines directly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.