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?
There are no annotations, so the description carries the full burden of behavioral disclosure. It does add a useful side-effect disclosure ('The body is discarded'), but it does not explain the result format, error behavior, or how invalid JSON is signaled, which matters for a validation tool.
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?
Two short, direct sentences with no filler. The main action is front-loaded, and the behavioral note about discarding the body follows immediately. Every word earns its place.
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 annotations, no output schema, and a schema containing mostly irrelevant parameters, the description is too thin. An agent still cannot confidently determine which parameter to populate, what successful validation returns, or whether invalid JSON produces an error or a boolean result.
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?
Although schema description coverage is 100%, three of the four parameters (url, host, zone) are unrelated to JSON validation, creating confusion. The description does not clarify that the 'body' corresponds to the 'json' parameter, nor does it explain whether the other parameters are ignored or used in some way.
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's action ('Check whether a body is valid JSON') and a key behavioral outcome ('The body is discarded'). This distinguishes it from the sibling tools, which all concern URLs, zones, robots, or time rather than JSON validation.
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 given about when to use this tool versus alternatives, nor are any exclusions or prerequisites mentioned. The purpose implies 'use when you need to validate JSON,' but the description does not explicitly frame it as a validation step before other operations.
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.
Most tools are distinct, but there is overlap in the URL/time clusters: citation, normalize-url, and domain-shape all involve host/scheme data, while timezone and utc-time both return current time. The vague compatibility tool also adds ambiguity.
Names mix noun-only forms (citation, timezone), verb-object forms (inspect-robots, normalize-url, validate-json), and compound nouns (iana-zones, status-catalog, utc-time). There is no consistent naming convention across the set.
Ten tools is a reasonable count for a general-purpose utility server. Each tool is lightweight and the set is neither bloated nor too sparse.
The domain is an open-ended 'utility index,' so completeness is hard to define; it covers URL handling, time, HTTP status, robots, and JSON validation. However, obvious common utilities like hashing, encoding, or string operations are absent, leaving notable gaps for a general utility server.