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?
The description discloses the main behavior—returning origin, host, and path while dropping query and fragment—but it does not explain how the host, json, and zone parameters are handled or whether they are ignored. This leaves important behavioral ambiguity.
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 very concise and directly states the core function in a single sentence. No unnecessary words or redundant details are included.
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 a simple core function, but the presence of unrelated parameters and the lack of guidance on their usage make the description incomplete. An agent would likely be confused about what parameters to provide.
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 parameter descriptions are inconsistent with the tool's stated purpose. The json parameter is described as 'JSON text to validate' and zone as 'IANA timezone name', which are clearly unrelated to URL normalization. The description adds no useful clarification for these parameters.
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 returns origin, host, and path for a URL and that query and fragment are dropped. However, the input schema contains unrelated parameters like json and zone, which muddles the purpose.
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 does not explain when to use this tool versus alternatives such as timezone or validate-json. It only describes the operation, not the appropriate context or selection criteria.
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 have distinct purposes, such as byte-count, validate-json, and utc-time. However, citation and normalize-url both handle URLs, and timezone and utc-time both provide time information, which could lead to occasional misselection.
Names are mostly lowercase with hyphens, but the pattern is inconsistent: some are nouns (byte-count, timezone, status-catalog) while others are verbs (inspect-robots, normalize-url, validate-json). There is no clear verb_noun or noun_verb convention across the set.
11 tools is a reasonable count for a utility server covering URLs, time, robots, and data validation. Each tool does something different, though a few are quite trivial, making the set slightly broad but not excessive.
The tools cover several common utility categories like URL normalization, current time, timezone lookup, and JSON validation. However, the scope is broad and shallow, leaving obvious gaps such as domain lookups, HTTP request mechanics, or date formatting, and there is no unifying workflow to judge full coverage.