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?
Without annotations, the description carries full responsibility for disclosing behavior. It only mentions that identifiers are not retained, but does not state whether the tool modifies data, performs lookups, or returns any specific result. The behavioral scope is entirely unclear.
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, consisting of only two short sentences, and is front-loaded with the main verb phrase. It avoids unnecessary length, though its brevity comes at the cost of meaningful content.
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 lacks any output schema or description of the classification result, and the input parameters are not explained in relation to the tool's operation. An agent would be unable to infer the expected inputs, outputs, or side effects, making the definition incomplete.
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?
All four parameters have descriptions, so schema coverage is 100%, but the descriptions are generic and not tied to the tool's purported classification function. For example, 'url' is described as 'HTTPS URL to normalize or cite' which hints at unrelated capabilities and adds confusion rather than clarifying how the parameter is used.
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 'Show how this request is classified' but does not specify what classification means, what inputs are expected, or what the output looks like. It is vague and fails to distinguish the tool's purpose from siblings 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 alternative tools. The description gives no context for the intended use case or scenario, leaving the agent without direction on selection.
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.
Tools are individually distinct in their operations, but the mix of URL, timezone, JSON, and temperature topics creates confusion about which tool applies to a given task. Names like 'compatibility' and 'citation' are vague and could be misinterpreted.
Naming conventions are inconsistent: some tools use hyphenated lower-case (f-to-c, iana-zones), others use single words (citation, timezone), and some use multi-word phrases (normalize-url, validate-json). This lack of a uniform pattern reduces predictability.
At 11 tools, the count is within the typical range, but it feels excessive for a server ostensibly dedicated to Fahrenheit-to-Celsius conversion. The number is inflated by unrelated utilities, making the set poorly scoped for the stated purpose.
For a Fahrenheit-to-Celsius converter, there is only one relevant tool (f-to-c). Essential operations like Celsius-to-Fahrenheit, Kelvin conversions, or batch conversions are missing, so the domain coverage is severely incomplete.