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 are provided, the description carries the full burden of behavioral disclosure. The sentence 'The body is discarded' is a useful side-effect disclosure about data handling. However, it does not mention potential side effects like network requests, state changes, or what happens on invalid JSON, so the transparency is partial.
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 core purpose front-loaded and the side-effect statement earning its place. There is zero filler, and the structure makes the key fact immediately visible.
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 4 optional parameters and no output schema, the description is underspecified. It does not clarify how url, host, and zone relate to the validation, nor what the tool returns for valid vs invalid JSON. An agent would have to guess about the tool's other parameters and return behavior.
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 input schema has 100% description coverage, so the baseline is 3. The description adds no parameter-specific meaning beyond the schema; the term 'body' loosely maps to the 'json' parameter, but it does not explain the roles of url, host, and zone. The schema already does the heavy lifting for parameter semantics.
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 operation: 'Check whether a body is valid JSON.' The verb + resource combination is specific and distinct from the sibling tools, which all relate to URLs, time, or citation rather than JSON validation. There is minor ambiguity in the word 'body' not being tied to the 'json' parameter, but the intent is unambiguous.
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 gives no guidance on when to use this tool instead of the sibling tools. It states what the tool does but provides no context, prerequisites, or exclusions that would help an agent decide between validate-json and alternatives.
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 different outputs, but there are overlapping clusters: citation, normalize-url, and domain-shape all deal with URL/hostname shapes, while timezone, utc-time, and iana-zones all cover time. A couple of vague names like compatibility and citation could also cause initial misselection.
All names are lowercase hyphenated, which gives a visual consistency, but they mix verb-led names like normalize-url and validate-json with noun-led names like status-catalog and timezone. The pattern is readable but not predictable enough to be considered strongly consistent.
11 tools is within a reasonable range and none feels redundant, but the collection spans several unrelated concerns such as URL handling, HTTP status, time, JSON validation, and robots.txt. It is slightly broad, though not bloated.
The set covers common lookups like URL normalization, status codes, time, and JSON validation, but there is no coherent domain that ties these together. Obvious adjacent operations such as redirect checking, response header inspection, or rate-limit-specific details are missing, leaving an agent with possible dead ends.