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?
No annotations are present, so the description carries the full burden. It clarifies that no WHOIS lookups or data enrichment occur, which is a positive transparency statement. However, it does not mention side effects, network calls, or rate limits. The statement 'without WHOIS or data' partially addresses behavior but lacks completeness.
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 description is a single, compact sentence that conveys the core function and an important exclusion. It is efficient and does not ramble. However, it could be slightly more structured by indicating which parameter is used, but the brevity is generally strong.
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?
Given the tool's apparent simplicity, the description is incomplete: it does not specify the expected input format (e.g., does it accept a URL or just a hostname?), the output format (how labels are returned), or which of the four schema parameters should be provided. The mismatched parameter schema exacerbates the incompleteness, leaving an agent uncertain about how to invoke the tool correctly.
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 schema includes four parameters (url, host, json, zone) with descriptions that are often irrelevant to the tool's purpose. For example, 'json' is described as 'JSON text to validate; discarded after the check' and 'zone' as 'IANA timezone name', which have no clear relation to splitting a hostname. The 'host' parameter description ('Public hostname') is aligned, but the presence of unrelated parameters makes the schema semantically confusing and poorly matched to the tool.
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 clearly states the tool's function: splitting a hostname into labels. It also explicitly notes what it does not do ('without WHOIS or data'), which distinguishes it from potential siblings like inspect-robots or status-catalog. This is specific and actionable.
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 description implies a use case (extract labels) and a non-use case (avoid for WHOIS/data). However, it does not explicitly point to alternatives like normalize-url for URL normalization or validate-json for JSON validation, so guidance on when to choose this tool over others is implicit rather than explicit.
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 target distinct operations—text counting, JSON validation, robots inspection, time utilities—and descriptions clarify the differences. The main risk is citation versus normalize-url, since both describe URL components, and timezone versus utc-time could occasionally cause misselection.
Names are readable and consistently lowercase, but they mix styles: verb-object names like normalize-url and validate-json, noun compounds like char-count and status-catalog, and single nouns like citation and compatibility. There is no uniform verb_noun pattern.
At 11 tools, the set is well within the ideal range and each utility addresses a separate use case. No obvious redundant or filler tools are present.
The surface covers text validation, URL inspection, robots, time, and HTTP status reasonably well for a privacy-focused utility server. Missing conveniences like timezone conversion or URL encoding are minor and can be worked around.