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Vet a dependency before install (paid $0.003)

deps_check

Vet a software package before installing: check for CVEs, deprecation, typosquatting, and license issues. Get a plain-English risk verdict.

Instructions

PAID $0.003 (x402, USDC on Base). Vet a software package BEFORE installing it. Returns existence, latest-vs-requested, deprecation/yank markers, known CVEs from OSV.dev, typosquat similarity to popular names, SPDX license, and age/download signals with a plain-English risk verdict. Args: ecosystem (npm|pypi|crates), name, version (optional). Without payment returns the x402 challenge; pass x_payment to settle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name, e.g. 'lodash', 'requests', 'serde'.
versionNoSpecific version to check for CVEs (optional; defaults to latest).
ecosystemYesPackage ecosystem: npm | pypi | crates.
x_paymentNox402 payment payload (base64) for this PAID check. If supplied it is forwarded as the X-PAYMENT header to settle the call and return the real result instead of a 402 challenge. Omit to get the price challenge first.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that this is a PAID operation ($0.003, x402, USDC on Base), lists the return data, and explains the 402 challenge behavior. It also notes that without payment you get a challenge and with x_payment you get the real result—transparent about the payment gating.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: price, purpose, return data, args, and payment flow. It is front-loaded with the most critical info (PAID) and avoids filler. The list of return signals is comprehensive without being verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description compensates by fully enumerating the returned signals. It also covers the payment intricacies and the optional version parameter. For a moderately complex tool, this is complete and actionable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes all four parameters with 100% coverage, including the enum for ecosystem and the default for version. The description enumerates the required args but adds little beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb: 'Vet a software package BEFORE installing it.' It enumerates the exact signals returned (existence, CVEs, deprecation, typosquat, license, age/download) and distinguishes it from siblings (x402_preflight, json_repair, url_read) which are unrelated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a clear usage context: use before installing a package, and it explains how to handle the payment flow (omit x_payment to get a challenge, pass it to settle). It does not explicitly name alternatives or exclusions, but the tool is unique among siblings, so clear context suffices.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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