x402-pip-audit
x402-pip-audit: Python package security audit. 🆓 5 free trial calls per registered wallet
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Name to process | |
| package | No | Package to process | |
| version | No | Version to process |
x402-pip-audit: Python package security audit. 🆓 5 free trial calls per registered wallet
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Name to process | |
| package | No | Package to process | |
| version | No | Version to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It adds only pricing/trial context (5 free calls) and never explains what the audit does, whether it queries a registry, what it returns, or whether it has side effects. This is a significant gap for a security-oriented tool with no output schema.
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 very short and easy to scan, and the trial-call note is useful context in the x402 ecosystem. The only minor waste is repeating the tool name as a prefix before the actual content.
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 no annotations and no output schema, this description is underspecified. An agent still doesn't know what the audit output looks like, which inputs are required or mutually exclusive, or how 'name' relates to 'package'. The agent would have to guess or invoke blindly.
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 a baseline of 3 is appropriate even without parameter info in the description. However, the schema's own descriptions are generic ('Name to process', 'Package to process', 'Version to process') and the tool description provides no disambiguation between 'name' and 'package'.
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 and resource: it audits Python package security. This distinguishes it from siblings like x402-npm-audit and generic AI audit tools, though some specificity is lost because it doesn't say what the audit report contains.
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 explicit guidance is given about when to use this tool versus alternatives such as x402-npm-audit, x402-ai-audit, or general security scanners. The phrase 'Python package' implies pip-related usage, but there are no exclusions, conditions, or named 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.
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.