Return origin, host, and path for a URL. Query and fragment are dropped.
| 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?
With no annotations, the description must carry behavioral disclosure. It does so by stating that query and fragment are dropped, which is non-obvious and important. It does not mention failure modes or return formatting, but for a normalization operation the key behavior is adequately conveyed.
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 two terse sentences with no filler. The core output is front-loaded, and the important dropped-parts behavior is given in the second sentence. It is optimally concise.
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?
The tool has four parameters, but the description only explains behavior related to 'url'. The purpose of 'host', 'json', and 'zone' is left unexplained, and there is no guidance on how these interact with normalization. This is a significant gap for an agent deciding what arguments to supply.
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%, so each parameter already has a description. The tool description adds no parameter-specific meaning and, in fact, only addresses the 'url' parameter while ignoring 'host', 'json', and 'zone' entirely, leaving the agent to reconcile the mismatch.
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 states a specific verb and resource: 'Return origin, host, and path for a URL' and adds the behavioral detail that query and fragment are dropped. This clearly distinguishes it from a generic URL validator or fetcher.
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 use when the agent needs the origin, host, or path with query and fragment removed, so the context is somewhat clear. However, it does not explicitly say when to prefer this over siblings like 'url-clean' or 'http-validate', leaving the agent to infer the distinction.
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.
json-ok and validate-json are effectively duplicate JSON validity checks, and several URL/HTTP tools (citation, normalize-url, domain-shape) have overlapping hostname/status concerns. compatibility is too vague to distinguish reliably from the other HTTP-related tools.
The names are all lowercase hyphenated, but they mix noun phrases (citation, timezone), imperative verbs (normalize-url, validate-json), and adjective constructions (json-ok). There is no consistent verb_noun or resource_action pattern, and the same JSON operation is called two different names.
Eleven tools is not inherently too large, but the set is not tightly scoped to the apparent 'JSON validity' purpose and bundles URL, robots, HTTP, and time utilities. The duplicate JSON validation tools also make the count feel padded rather than earning their places.
There are useful clusters for URL inspection, HTTP status, time, and JSON validity, but no clear workflow connects them and compatibility remains unexplained. Within a broad safe-web/utility interpretation the basics are present, yet obvious helpers such as header inspection or a single unified JSON validator are missing.