Short catalog of common HTTP status codes.
| 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 carries the full burden of behavioral disclosure, but it only says 'short catalog' without stating what the tool does with the supplied parameters, whether it filters or summarizes, or what output format to expect. It does not disclose side effects, limitations, or return behavior.
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 brief and has no filler words, but it is under-specified rather than concisely complete. It omits the behavioral and contextual information an agent needs, so the brevity comes at the cost of usefulness.
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, no output schema, and no annotations, yet the description explains none of these parameters' role or the expected result. An agent would struggle to invoke this tool correctly because the connection between inputs and the 'short catalog' is entirely missing.
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 description coverage is 100%, so each parameter (url, host, json, zone) has an individual description, reaching the baseline of 3. However, the tool description does not explain how any of these parameters relate to an HTTP status code catalog, leaving the overall purpose of the parameters unclear.
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 identifies the subject (common HTTP status codes) and implies the tool returns a catalog, but it lacks an explicit verb and does not clarify what action is performed. It also does not differentiate this tool from sibling tools like validate, normalize, or citation, so an agent cannot confidently distinguish it.
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?
There is no guidance on when to use this tool versus alternatives. The description provides no context about the problem it solves or which inputs are appropriate, leaving the agent to guess from the parameter names.
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.
Several tools overlap around URL inspection (citation vs normalize-url) and time (timezone vs utc-time), though their descriptions clarify differing outputs. The vague compatibility tool adds some ambiguity about when it should be selected.
All names are lowercase and hyphenated, which is a consistent style, but the pattern mixes noun phrases like citation and timezone with verb-led names like normalize-url and validate-json. This is readable but does not follow one predictable convention.
Eleven tools is a reasonable size for this collection of privacy-preserving utility functions. The count is not bloated, though the time-related tools could arguably be consolidated.
The tool surface covers the apparent domain of inspecting data shapes without retaining content: URLs, email, JSON, robots.txt, time, and HTTP status. Minor gaps like direct header or DNS inspection exist, but common workflows are not dead-ended.