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 carries the behavioral burden and it does disclose one useful trait: the body is discarded after the check. However, it does not state what the tool returns for valid versus invalid JSON, nor any error behavior, leaving a meaningful 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?
Two short sentences with the main action front-loaded and the side-effect stated second. Every word earns its place; there is no filler or repetition.
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?
The tool has no output schema and no annotations, yet the description never explains the return value (boolean, error, etc.) the caller receives. The schema also lists url, host, and zone parameters that appear unrelated to JSON validation, and the description does not clarify their role, so an agent cannot fully anticipate the call.
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 url, host, json, and zone are already documented structurally. The description adds no parameter-level meaning beyond the discard hint already present in the json parameter's schema description, so the baseline 3 applies.
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 verb ('Check') and resource ('whether a body is valid JSON'), so an agent immediately knows this is a validation operation. It does not explicitly differentiate it from sibling tools like normalize-url or validate-json, so it stops short of a 5.
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 on when to use this tool versus alternatives, and no prerequisites or context are mentioned. The only implication is that validation might be a gate before other processing, but that is left entirely to inference.
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 distinct purposes, but URL-related tools like citation and normalize-url and time tools like timezone and utc-time occupy adjacent territory. Descriptions are clear enough that an agent can usually select correctly.
All names are lowercase hyphenated, which provides a consistent visual style. However, the set mixes noun-style names (citation, timezone), verb-style names (normalize-url, validate-json), and abbreviation-style names (km-to-mi), so there is no uniform verb_noun convention.
A server named 'Kilometers to miles' having 11 tools is excessive, since only km-to-mi relates to the stated purpose. The count could work for a general utility server, but it does not fit the apparent conversion-focused scope.
As a unit conversion service, the server is severely incomplete: it only supports kilometers-to-miles and lacks the reverse conversion or other conversions. As a utility collection, the tools are unrelated and form no coherent domain whose completeness can be judged.