Check whether a body is valid JSON. The body is discarded.
| 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 responsibility for behavioral disclosure. It does add a meaningful detail: the body is discarded. However, it omits the return format or error behavior, and there is no output schema, so the agent cannot know what result to expect beyond a pass/fail check.
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 extremely concise with two sentences, both of which earn their place: the primary purpose and a key side effect. It is front-loaded and free of filler.
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 no output schema and four schema properties, the description is underspecified. It doesn't state what result is returned for valid versus invalid JSON, which parameter to use, or that the other parameters are unused. This leaves the agent without enough information to invoke the tool confidently.
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 baseline is 3. The description adds no additional parameter meaning; it refers to a 'body' without connecting it to the `json` parameter. The schema already documents each property, so the description neither compensates for nor detracts from parameter understanding.
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 clearly states the tool checks whether a body is valid JSON and that the body is discarded. This distinguishes it from sibling operations that normalize, cite, or check status. However, 'body' is vague and doesn't explicitly map to the `json` parameter in the schema.
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 the listed siblings, such as normalize-url or status checks. It also doesn't clarify that the `json` parameter is the intended input and that the other schema properties (url, host, zone) are irrelevant for this tool.
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.
The timezone tools overlap heavily: `timezone` already supports named IANA zones, making `utc-time` and `tz-america-new-york` largely redundant. The URL tools also blur together (`citation` vs `normalize-url` both return host/scheme information), and `compatibility` is too vague to confidently distinguish from the other request-classification tools.
All names share a lowercase hyphenated style, but they mix conventions: verb-prefixed tools like `inspect-robots`, `normalize-url`, and `validate-json` sit alongside noun phrases like `domain-shape`, `status-catalog`, and `utc-time`, plus single-word names like `timezone` and `citation`. The names are readable and predictable in format, but not in action pattern.
Eleven tools is not an extreme number, but the set is not well-scoped: it mixes time, URL normalization, HTTP status, robots.txt, and JSON validation concerns. The count feels like several small toolkits merged into one server rather than a focused tool collection where each tool clearly earns its place.
There is no coherent domain to call complete. As a clock server, it lacks timezone conversion, offsets, or date formatting; as a web utility server, it lacks common operations like fetching content or inspecting headers. Significant operations for either inferred purpose are missing, so agents would likely hit dead ends.