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America/Guatemala clock

domain-shape

Split a hostname into labels without WHOIS or ownership data.

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2/5.0
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. It only mentions that the tool avoids WHOIS/ownership data, but does not disclose that many parameters are 'discarded after the check' (as the schema notes), whether there are side effects, or what the output structure is. This is minimal and insufficient for a tool with 9 heterogeneous parameters.

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

Conciseness3/5

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

The description is a single concise sentence with no waste, which is good for conciseness. However, it is so brief that it omits essential context, making it more under-specified than efficiently concise. The structure is front-loaded but fails to cover the tool's full scope.

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?

For a tool with 9 parameters, no output schema, and no annotations, this description is extremely incomplete. It does not explain what the tool does with the other parameters, what the return value looks like, or how the tool behaves under different inputs. An agent would be unable to correctly invoke this tool for most of its parameters based on the description alone.

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 coverage is 100% and each parameter has a description in the schema, so the baseline is 3. The tool description adds no parameter-specific information beyond what the schema already provides. It does not clarify how the hostname-splitting function relates to the other parameters, but the schema descriptions are adequate for individual parameters.

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

Purpose2/5

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

The description states a specific action for the 'host' parameter ('Split a hostname into labels') and distinguishes it from WHOIS/ownership data, but it completely ignores the other 8 parameters (ref, url, city, feed, json, path, zone, query). An agent cannot infer the tool's overall purpose from this description, making it misleading for the majority of its inputs. It does not clearly state that this is a multi-purpose validation tool.

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 on when to use this tool versus its many siblings (e.g., github-repo-shape, figma-url-shape, normalize-url). The description offers no exclusions or alternative recommendations, leaving the agent to guess which of the 27 sibling tools is appropriate for a given input type.

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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