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check_package_vulns

Identify known vulnerabilities in a package by querying OSV.dev, with support for major ecosystems and optional version filtering.

Instructions

Check a package for known vulnerabilities via OSV.dev.

Args: package: Package name (e.g. log4j-core, requests, lodash) ecosystem: Package ecosystem — PyPI, npm, Go, Maven, NuGet, crates.io, Packagist, Hex, RubyGems, Android, CocoaPods, GitHub Actions version: Optional specific version to check (e.g. 2.14.1)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYes
ecosystemYes
versionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description bears full burden. It mentions using OSV.dev but does not disclose behavior beyond the basic check (e.g., rate limits, response format, or any side effects). The lack of details on what constitutes a 'vulnerability' or how results are returned limits transparency.

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 short and structured with a one-line summary followed by parameter documentation. It avoids fluff but could be slightly more concise by omitting the 'Args:' prefix since the schema already defines parameters.

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

Completeness3/5

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

An output schema exists but is not referenced in the description. The description lacks any hint about the output (e.g., list of CVEs, severity scores), which would help the agent understand what to expect. It is minimally adequate but not complete for a tool that returns structured data.

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

Parameters4/5

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

Schema description coverage is 0%, so the description compensates well by listing parameters with examples (e.g., package: 'log4j-core', ecosystem: 'PyPI', version: '2.14.1'). It adds enumerations for ecosystem and clarifies optionality, though it could further explain version format or response linkage.

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 checks a package for known vulnerabilities via OSV.dev. It specifies the action ('check') and resource ('package for vulnerabilities'), and the tool name and examples distinguish it from sibling tools like 'lookup_cve' or 'scan_dependencies'.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or when not to use it, leaving the agent without decision-making context.

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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