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Aayat AI

Package check ($0.005)

package-check
Read-only

Should a coding agent install this package? Checks one npm, PyPI, crates or Go package version for known vulnerabilities and malware (OSV.dev), deprecation, typosquat look-alike names, install scripts, licence, downloads, release activity and OpenSSF Scorecard, then gives a verdict (ok/caution/avoid), a 0-100 score and every reason. Price: $0.005 in USDC per call (x402 or prepaid credits). In the free trial.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name, e.g. express, requests, serde or github.com/gin-gonic/gin.
versionNoExact version to check (default: the latest release).
ecosystemNoPackage ecosystem: npm, pypi, crates (Rust) or go (Go modules).npm

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
repoNoGitHub stars, forks, open issues and OpenSSF Scorecard (0-10).
flagsYesEvery reason behind the verdict.
scoreYes0-100, higher is safer.
sourcesNo
verdictYes
versionYesVersion checked.
isLatestNo
licencesYes
releasesNolatest, latestPublishedAt, firstPublishedAt, versions, releasesLast365Days.
checkedAtNo
ecosystemYes
deprecatedNo
repositoryNo
descriptionNo
licenceKindNo
lookalikeOfNoPopular packages this name resembles (typosquat check).
maintainersNo
latestVersionNo
installScriptsNonpm install hooks that run code on install.
vulnerabilitiesYesKnown vulnerabilities in this version (most severe first, up to 25).
weeklyDownloadsNo
vulnerabilityCountsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint=false. The description adds substantial behavioral context: it lists all checks performed, the output verdict/score/reasons, and crucially the pricing model ($0.005 in USDC per call via x402 or prepaid credits, free trial). This goes well beyond what annotations provide.

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

Conciseness4/5

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

The description is front-loaded with a hook question, followed by a dense list of checks, a summary of outputs, and a separate pricing sentence. Every sentence earns its place, though the list is long and could be slightly trimmed without losing meaning.

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 that an output schema exists, the description need not detail return values, yet it helpfully summarizes the verdict, score, and reasons. It also covers pricing and the full set of checks, making it complete for an agent to understand scope, cost, and behavior.

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?

Schema description coverage is 100%, so the schema fully documents all three parameters (name, version, ecosystem). The description adds no additional syntax, format, or constraint details beyond restating the ecosystems and single-package scope. Baseline 3 applies when the schema does the heavy lifting.

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

Purpose4/5

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

The description states a specific verb and resource: checks one package version for a comprehensive set of security and health signals. It clearly scopes to a single package across four ecosystems. However, it does not explicitly differentiate itself from siblings like package-audit, dependency-report, or dependency-verdict, leaving some ambiguity.

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?

The opening question 'Should a coding agent install this package?' provides a clear usage context for decision-making before installation. Yet it does not name alternatives or specify when not to use this tool versus related siblings such as package-audit or dependency-report.

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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