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Whether current UTC month is US DST-heavy

dst-us2

Whether current UTC month is US DST-heavy

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoGit ref name; discarded after the shape check
urlNoHTTPS URL to normalize or cite
cityNoCity name for a public weather hint; discarded after the call
feedNoPublic RSS or Atom URL; titles discarded
hostNoPublic hostname
jsonNoJSON text to validate; discarded after the check
pathNoFile path to check; no disk access
zoneNoIANA timezone name
queryNoSearch text; discarded after the length check
websiteNoPublic https website (homepage domain) to check for agent readiness: robots.txt, llms.txt, sitemap.xml, extractable HTML. Bounded, identified, robots-respecting.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / website
      Added value: +{
      +  "description": "Public https website (homepage domain) to check for agent readiness: robots.txt, llms.txt, sitemap.xml, extractable HTML. Bounded, identified, robots-respecting.",
      +  "type": "string"
      +}
  2. First observed

TDQS

C2.4/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only says 'current UTC month' and gives no indication that all inputs appear to be ignored or discarded. It also fails to state the return value or clarify how 'DST-heavy' is computed, leaving important behavioral traits undisclosed.

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 text is a single, front-loaded sentence with no filler or repetition. It is concise, though it sacrifices needed clarification for brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 10 optional parameters, no annotations, and no output schema, this sparse description is incomplete. It does not define the boolean result, explain 'DST-heavy', or clarify that the parameters are irrelevant, so an agent cannot confidently invoke or interpret the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although every parameter has a schema description, those descriptions are mostly unrelated to a US DST check and the main description does not bridge them to the tool's behavior. The agent receives no help determining which, if any, parameters are relevant, so the parameter semantics are more confusing than clarifying.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description communicates a specific predicate about the current UTC month and US DST, and it is somewhat distinct from sibling time tools like timezone and utc-time. However, 'DST-heavy' is never defined, and the tool's exact scope and return semantics are unclear, making the purpose vague rather than crisp.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use dst-us2 versus timezone, utc-time, or iana-zones. It also does not state that the tool needs no parameters or how it should fit into a larger workflow, leaving usage entirely to inference.

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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