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?
Since no annotations exist, the description carries the full burden of behavior disclosure. It does disclose that the body is discarded, which is a meaningful side effect, but it omits what happens on success or failure, whether errors are raised, and what response the caller receives. For a validation tool this is a significant transparency gap.
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 compact, with one sentence and two clauses and no filler. It front-loads the core validation action and efficiently states that the body is discarded. The brevity is appropriate for a simple tool, though it sacrifices behavioral detail.
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?
With no output schema, the description should explain how the validation result is conveyed, such as a boolean or error message. It also fails to account for the four optional parameters when only one appears relevant. An agent knows what is checked but not what the outcome looks like or how to interpret the unrelated parameters.
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 each parameter is already documented and the baseline is 3. The description adds no new parameter meaning and introduces 'body' as a term that doesn't map cleanly to the 'json' property. Unrelated parameters (url, host, zone) are not addressed in the description, but the schema handles them.
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?
States the specific action 'Check whether ... is valid JSON' on a body, making the validation intent clear. It is distinguishable from sibling tools that normalize URLs or handle timezones, though it never explicitly names an alternative. The term 'body' is slightly ambiguous relative to the schema's 'json' parameter, but the overall purpose is clear.
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 sibling tools. There is no mention of invocation context, prerequisites, or conditions that favor validate-json. An agent must infer which parameter to populate from the schema alone.
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 around URL inspection (citation vs normalize-url) and time (timezone vs utc-time), though their descriptions clarify differing outputs. The vague compatibility tool adds some ambiguity about when it should be selected.
All names are lowercase and hyphenated, which is a consistent style, but the pattern mixes noun phrases like citation and timezone with verb-led names like normalize-url and validate-json. This is readable but does not follow one predictable convention.
Eleven tools is a reasonable size for this collection of privacy-preserving utility functions. The count is not bloated, though the time-related tools could arguably be consolidated.
The tool surface covers the apparent domain of inspecting data shapes without retaining content: URLs, email, JSON, robots.txt, time, and HTTP status. Minor gaps like direct header or DNS inspection exist, but common workflows are not dead-ended.