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 must carry the full behavioral burden, but it only says the body is discarded—information already present in the json parameter description. It does not disclose the return format, error behavior, or whether the tool fetches a URL body as well as accepting direct JSON text.
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 short and front-loaded, stating the core action in the first sentence. The second sentence about discarding is a bit redundant with the schema but still concise.
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 validator with no output schema and four optional parameters from apparently unrelated domains, the description is incomplete. It does not explain what result the agent can expect (boolean, error, etc.), how to structure a valid call, or how this tool relates to the sibling URL and timezone tools.
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; each parameter already has an individual description. However, the main description adds no clarity about how the four unrelated optional parameters interact or which ones are actually needed for a validation call.
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 ('Check whether a body is valid JSON') and clearly identifies the resource being validated. It is not a tautology of the tool name, though 'body' is slightly ambiguous compared to the parameter named 'json'.
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, no mention of prerequisites, and no explanation of how the url, host, or zone parameters relate to validation. The only implied usage is that a JSON body is checked and discarded.
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 overlap in input or output: citation and normalize-url both inspect URLs, and timezone and utc-time both return current time. The descriptions clarify the intended focus somewhat, but an agent could still misselect between these pairs.
Tool names mix standalone nouns like citation and timezone with verb-noun compounds like inspect-robots and normalize-url, and hyphenation is inconsistent. Most names are still readable and weakly predictable, but there is no strong naming convention.
Ten tools is a moderate count, but the server's apparent 'Robots Inspect' focus is supported by only inspect-robots. The rest are broad URL, time, and JSON utilities, which makes the set feel padded rather than tightly scoped.
For a server named Robots Inspect, robots.txt coverage stops at group counts and offers no way to retrieve actual rules, test URLs against directives, or compare agent policies. The unrelated utilities do not fill this gap, so core inspection workflows would dead-end.