Show how this request is classified. No identifiers are retained.
| 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 provided, the description carries the full burden. It discloses a privacy-relevant behavior ('No identifiers are retained') but does not mention side effects, rate limits, or whether operation is read-only. The privacy statement is useful, but other behavioral aspects are left undisclosed.
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 short sentences with no fluff. The core purpose is front-loaded and the privacy note is brief and relevant.
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 4 optional parameters and no output schema, and the description doesn't explain what 'classified' means or what the output looks like. An agent would not know what to expect from the result or how the parameters influence it. This is a significant gap for a tool that appears to be a checker.
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 the schema already documents each parameter. The description adds no extra meaning beyond the schema fields; it doesn't clarify how the parameters relate to classification or whether they are alternatives or combined. Baseline 3 is appropriate because the schema does the heavy lifting.
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 action ('Show how this request is classified') and a key behavioral constraint ('No identifiers are retained'). It distinguishes itself from siblings by implying classification rather than citation or normalization, though it doesn't explicitly name any alternative.
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 gives no guidance on when to use this tool versus the siblings like citation or validate. It does not state preconditions or scenarios where this tool is preferred, so an agent has little help in choosing among the many sibling tools.
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 fall into overlapping clusters: citation and normalize-url both process URLs, while timezone, utc-time, and unix-now all report current time. The descriptions clarify the exact output of each, but an agent could still struggle to pick between them without careful reading.
All names are lowercase and hyphenated, giving a consistent visual style. However, the pattern mixes noun phrases (citation, status-catalog, unix-now) with verb-noun imperatives (inspect-robots, normalize-url, validate-json), so there is no single predictable naming convention.
Eleven tools is within the typical well-scoped range and each utility has a narrow function. The count itself is reasonable, although the server name suggests a time-focused purpose while many tools are actually web/URL utilities.
The set covers common stateless operations: current time, timezone lookups, URL inspection, robots, and JSON validation. Notable gaps include epoch-to-human timestamp conversion and a way to fetch or inspect HTTP response details beyond status, which would be expected in a general utility server.