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 discloses that the body is discarded, which is valuable, but with no annotations and no output schema it fails to explain what the tool returns (e.g., a boolean, error messages, or HTTP status). This is a significant behavioral gap for an agent trying to use the result.
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 sentences with no filler. The core purpose is front-loaded and the discarded-body behavior is communicated in a single extra clause. 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 straightforward validation tool, the description is under-specified because it omits the return format and any error-handling details. Without an output schema or annotations, an agent cannot confidently determine what success or failure looks like after invoking the tool.
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 coverage is 100%, so the parameters are already documented in the schema. The description adds no parameter-level meaning, but the baseline of 3 is appropriate because the description does not need to repeat what the schema already provides.
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 action ('check') and resource ('whether a body is valid JSON'), which is specific and not a tautology. It also adds the useful consequence that the body is discarded. There are no similar siblings to differentiate from, so this is clear enough.
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 about when to use this tool versus alternatives, and no mention of when not to use it. The intended use is implied but never stated explicitly in terms of selection criteria or exclusions.
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 target clearly distinct resources: URL components, IP version, robots.txt, time zones, JSON, and HTTP status codes. However, citation and normalize-url both operate on URLs, and compatibility is vague enough that an agent might pause to determine which tool applies.
All names use lowercase hyphenation, which gives a consistent visual style. However, the set mixes verb-object names like normalize-url and validate-json with noun compounds like status-catalog and iana-zones, so there is no consistent semantic naming pattern.
Eleven tools is a reasonable number for a utility server, and each tool performs a distinct small task. The scope is broad—covering URLs, HTTP, time, and JSON—so it feels less tightly focused than a single-domain server.
The set covers simple URL inspection, HTTP status lookup, current time retrieval, and JSON validation. Natural gaps remain, such as timezone conversion, HTTP header inspection, or deeper network diagnostics, so the surface is useful but not comprehensive.