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?
The description mentions 'The body is discarded,' which gives a minimal hint about side-effects (no persistence). However, it does not disclose how the other parameters (url, host, zone) are used or whether any side-effects occur. With no annotations, the description carries full responsibility but falls short of transparency.
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 very brief (single sentence), which is concise in word count. However, the brevity omits essential context about parameters and usage, making it efficient but lacking necessary detail. The structure is straightforward but not well-rounded.
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?
Given the schema has 4 parameters with confusing descriptions, the description is severely incomplete. It does not clarify the purpose of each parameter, how they interact, or what the expected input/output format is. This leaves an agent unable to correctly invoke the tool without additional assumptions.
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?
The schema descriptions are largely mismatched with the tool's purpose (e.g., 'url' described as 'HTTPS URL to normalize or cite' and 'zone' described as 'IANA timezone name'). The description only references 'body' but does not explain the actual parameters or their roles. This provides no meaningful semantic guidance.
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 clear purpose: 'Check whether a body is valid JSON.' This identifies the primary action and resource (JSON validation) without ambiguity. However, it does not explicitly differentiate from sibling tools, though the tool name and purpose are sufficiently distinct.
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 provides no guidance on when to use this tool versus alternatives. It does not mention any conditions, such as 'use this when you need to verify JSON syntax' or compare with sibling tools like normalize-url or timezone. There is no usage context at all.
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 have clearly distinct purposes, but there is some overlap between URL-related tools (citation vs normalize-url) and time-related tools (timezone vs utc-time). Overall, the descriptions are clear enough to avoid major confusion.
The naming is inconsistent: some tools are nouns (citation, timezone, status-catalog), some are verb phrases (inspect-robots, normalize-url, validate-json), and one is an abbreviated pair (lang-fr). The hyphenated style is consistent, but the verb/noun mix makes the set feel incoherent.
With 11 tools, the count is within a reasonable range and each tool adds a distinct capability. It is not overly large, though some tools feel like a grab-bag of unrelated utilities.
The tools cover a variety of network, time, and data-validation utilities, but the scope is unclear. Given the server name 'Language fr', there is very little language-specific functionality beyond lang-fr, so the completeness relative to the implied domain is limited.