x402-json-pretty
JSON Pretty: JSON Pretty
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
JSON Pretty: JSON Pretty
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure, and it reveals nothing: not how JSON input reaches a 0-parameter tool, what transformation 'pretty' performs (indentation, sorting, key order), or what the output shape is. For a tool that must accept input somehow, this is a complete silence.
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?
At five words, the description is pathologically under-specified rather than concise — there is no useful content to front-load. Every word is wasted restating the tool name in a label-plus-value format.
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 annotations, no output schema, zero parameters, and roughly 1,800 siblings including near-duplicates like x402-json-format and x402-json-minify, the description must do all the work of explaining invocation and output, and it does none of it. An agent has no way to call this tool correctly.
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?
There are zero parameters, so the schema has nothing to document, but the description also fails to explain how the JSON payload is supplied to a 0-param tool. The vacuous 100% schema coverage earns no credit, and the input-channel ambiguity keeps this at the baseline-4 floor minus one.
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 is 'JSON Pretty: JSON Pretty' — a pure tautology that restates the tool's own name with no verb and no resource. It conveys only that the tool relates to JSON prettiness and does nothing to distinguish it from siblings like x402-json-format or x402-json-minify.
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 statement of when to use this tool versus alternatives. With dozens of JSON-related siblings (x402-json-format, x402-json-minify, x402-json-toolkit, x402-xml-pretty) and no exclusions or routing hints, an agent cannot choose it correctly.
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.
The tool set is saturated with near-duplicates and synonyms: character-count vs char-count, clamp vs clamp-value, is-abundant vs is-abundant-num vs is-abundant-number, and fetch vs browser-scrape vs web-scrape vs text-scrape. Generic names like 'difference', 'normalize', 'range', and 'partition' make the boundaries even harder for an agent to determine.
Most tools share a x402- kebab-case prefix, but the set mixes noun-only names (math, hash, prime, time), verb-first names (get_stats, find, validate), auto-generated names (x402-publish-1787853294312-base-account), and inconsistent variants like temp vs temperature vs temperature-convert. This is not a coherent verb_noun convention despite the common prefix.
1677 tools is an extreme count that creates selection paralysis and makes coherent agent use impractical. A utility or marketplace server at this scale needs sub-services or namespacing rather than a flat tool list.
The surface has broad token coverage across many utility categories, but the marketplace aspect is incomplete: service_discovery and get_stats exist, yet there are no generic publish, update, delete, or account-management operations. Utility families also contain redundant variants without clear completion or lifecycle structure.