Split a hostname into labels without WHOIS or ownership data.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | HTTPS URL to normalize or cite | |
| host | No | Public hostname | |
| json | No | JSON text to validate; discarded after the check | |
| zone | No | IANA timezone name |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It discloses one limitation (no WHOIS/ownership data) but says nothing about output format, error handling, or side effects, and does not clarify which input parameter feeds the hostname.
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 sentence is tight and front-loaded with the core action, so there is no clutter. But the tool has a four-parameter schema and no output schema; one sentence is too terse to be fully actionable, so this leans toward under-specification.
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?
With no annotations and no output schema, the description should explain behavior and parameters, yet it covers only the split operation. The presence of json and zone params is unexplained, making the tool's actual scope ambiguous and incomplete for an agent.
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 coverage is 100%, but the schema describes four unrelated params (url, host, json, zone). The description only adds meaning to the host param as the input for splitting; url, json, and zone remain unexplained and appear inconsistent with the described operation, leaving the agent unsure what to supply.
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?
The description names a specific verb and object: split a hostname into labels, which is immediately understandable. It also adds a negative scope boundary ('without WHOIS or ownership data'), but it doesn't explicitly differentiate itself from sibling tools such as normalize-url or validate-json.
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 'split a hostname into labels' implies when to use it, and 'without WHOIS or ownership data' gives a clear exclusion. However, it never states when to prefer an alternative sibling tool or what conditions make this the wrong choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are distinct, but there is overlap in the URL/time clusters: citation, normalize-url, and domain-shape all involve host/scheme data, while timezone and utc-time both return current time. The vague compatibility tool also adds ambiguity.
Names mix noun-only forms (citation, timezone), verb-object forms (inspect-robots, normalize-url, validate-json), and compound nouns (iana-zones, status-catalog, utc-time). There is no consistent naming convention across the set.
Ten tools is a reasonable count for a general-purpose utility server. Each tool is lightweight and the set is neither bloated nor too sparse.
The domain is an open-ended 'utility index,' so completeness is hard to define; it covers URL handling, time, HTTP status, robots, and JSON validation. However, obvious common utilities like hashing, encoding, or string operations are absent, leaving notable gaps for a general utility server.