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, but 'Short catalog' only hints that this is a static reference. It does not disclose what the parameters do, whether any inputs affect the catalog, or what the agent can expect as a result.
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 minimal and front-loaded with the subject matter, but it is under-specified rather than efficiently complete. A single noun phrase does not give the agent enough structure to act on.
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?
For a tool with 4 optional parameters, no output schema, and unclear parameters relative to its stated purpose, the description is fundamentally inadequate. An agent cannot determine what inputs to supply or what the tool returns.
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?
Although schema coverage is 100%, the parameter descriptions (URL to normalize, hostname, JSON to validate, timezone zone) appear copied from sibling tools and are semantically unrelated to an HTTP status code catalog. The description adds no clarification, so the baseline of 3 is not earned.
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 resource (catalog of common HTTP status codes) and is clearly distinct from siblings like validate-json or timezone. However, it uses no operation verb — 'catalog' as a noun does not say whether the tool lists, looks up, or explains status codes, leaving the agent to infer the action.
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, nor how to invoke it. An agent gets no context about whether to pass a code, search for one, or request the full list.
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 clearly distinct resources: URL components, IP version, robots.txt, time zones, JSON, and HTTP status codes. However, citation and normalize-url both operate on URLs, and compatibility is vague enough that an agent might pause to determine which tool applies.
All names use lowercase hyphenation, which gives a consistent visual style. However, the set mixes verb-object names like normalize-url and validate-json with noun compounds like status-catalog and iana-zones, so there is no consistent semantic naming pattern.
Eleven tools is a reasonable number for a utility server, and each tool performs a distinct small task. The scope is broad—covering URLs, HTTP, time, and JSON—so it feels less tightly focused than a single-domain server.
The set covers simple URL inspection, HTTP status lookup, current time retrieval, and JSON validation. Natural gaps remain, such as timezone conversion, HTTP header inspection, or deeper network diagnostics, so the surface is useful but not comprehensive.