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 full burden but gives no information about return format, side effects, errors, or whether the tool is read-only. The behavior is entirely opaque.
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 concise in length but inefficient in conveying meaning. It is too terse to be useful, and the single sentence does not clarify the tool's operation or scope.
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 description is completely inadequate for an agent to understand when and how to call this tool. It lacks essential context about the tool's purpose, parameters, and expected output.
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 in the schema are generic and appear mismatched with the stated purpose (e.g., url is described as 'to normalize or cite', which suggests a different tool). The description does not explain how url, host, json, or zone relate to HTTP status codes.
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 is a noun phrase ('catalog of common HTTP status codes') and does not specify the action the tool performs. It is ambiguous whether it lists, searches, or categorizes status codes, and it does not differentiate from sibling tools like validate-json or normalize-url.
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?
No guidance is provided on when to use this tool versus alternatives. The description offers no context or conditions for invocation.
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 operate on URLs (citation, normalize-url, domain-shape, compatibility) with overlapping inputs but different outputs, and hour-utc vs utc-time are close in purpose. Descriptions mostly clarify the distinction, but an agent could easily select the wrong URL-analysis tool without deeper inspection.
Names mix single nouns (citation, compatibility, timezone) with hyphenated compounds that alternate verb-first (normalize-url, inspect-robots) and noun-first (status-catalog, domain-shape). No consistent verb_noun or action-object pattern is present.
Eleven tools is within a reasonable count range, but the set spans time, URL parsing, HTTP status, and JSON validation with no unifying scope. Several tools feel like a grab-bag rather than a focused server, so the count is not fully earned.
The time tools lack basic conversions or formatting options, and the URL tools have no clear workflow for fetching, parsing, and classifying content. For the apparent 'web/URL utilities plus time' scope, there are significant gaps that would force agents to work around missing functionality.