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Rankine to kelvin, number discarded

by io.github.sadri-dridi
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r-k

Rankine to kelvin, number discarded

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

D1.3/5.0
Behavior1/5

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 'number discarded'. It does not disclose the return format, which input is meaningful, side effects, or whether any value is actually returned after the conversion.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded, but it is under-specified rather than appropriately concise. 'Rankine to kelvin, number discarded' with no schema alignment leaves too much essential meaning absent.

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

Completeness1/5

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

This tool has 10 optional parameters, no annotations, and no output schema, so the description must compensate. It provides no output semantics, no input guidance, and no relationship between the conversion and the schema, making the definition severely incomplete.

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?

The input schema has 100% description coverage for its ten parameters, but none of them represents a Rankine temperature value. The tool description adds no mapping between the stated conversion and any parameter, so an agent cannot determine what to pass.

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

Purpose1/5

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

The description names a conversion direction ('Rankine to kelvin') but has no verb, no temperature parameter in the schema, and adds the confusing qualifier 'number discarded'. An agent cannot tell whether this converts, validates, or returns a value, and the schema's unrelated string parameters make the stated purpose misleading.

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

Usage Guidelines1/5

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

There is no guidance about when to use this tool versus the many shape-check, URL, search, and timezone sibling tools. The description implies a unit conversion, but no alternative conversion tool is mentioned and no contextual trigger is given.

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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