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ZEN SecDB

by cloud.nttzen.secdb

purl_audit

Perform a software package vulnerability audit using SecDB.

What this tool does

Analyzes a list of software packages identified by PURL (Package URL) and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided.

When to use this tool

Use this tool when the user wants to determine:

  • whether application dependencies contain known vulnerabilities

  • whether a project is affected by security advisories

  • which packages require patching or upgrading

Supported ecosystems

Inputs

  • purls: list of Package URLs, one per entry. Generate them from your project manifest files:

    • Node.js: package.json / package-lock.json

    • Python: requirements.txt / Pipfile.lock / pyproject.toml

    • Ruby: Gemfile.lock

    • Go: go.mod / go.sum

    • Rust: Cargo.lock

    • PHP: composer.lock

    • Java: pom.xml / build.gradle

    • .NET: *.csproj / packages.lock.json

Outputs

  • report: structured JSON objects describing the advisories affecting the audited packages.

  • summary: Markdown summary including total vulnerabilities, severity breakdown, and key findings.

LLM usage guidelines

  • Never guess whether a package is vulnerable — always call this tool.

  • Only submit PURLs from the supported ecosystems listed above; others will be ignored.

  • The summary is already Markdown and can be shown directly.

  • Use report when deeper technical analysis is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
purlsYesList of Package URLs (PURL) to audit

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reportNo
summaryNoMarkdown summary including total vulnerabilities, severity breakdown, and key findings

TDQS

A5/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses that results are based exclusively on the provided package list, that unsupported ecosystem PURLs will be ignored, and what outputs to expect (report and summary). This provides strong behavioral transparency beyond basic operation.

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 well-structured with clear headings (What, When, Supported ecosystems, Inputs, Outputs, LLM guidelines). While lengthy, every section earns its place by providing essential details like examples and manifest file sources. It is informative without being redundant.

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 tool's complexity (one parameter but many ecosystem variations), the description is complete: it covers supported ecosystems, input generation, output format, and usage rules. With an output schema present, it doesn't need to detail return structure, but it still mentions the two outputs. This is exceptional contextual coverage.

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

Parameters5/5

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

Although schema coverage is 100%, the description adds substantial meaning: it explains what PURLs are, gives examples for each supported ecosystem, and lists manifest files to generate them from. This goes far beyond the schema's bare 'List of Package URLs' and gives the agent concrete guidance for constructing valid inputs.

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 performs a vulnerability audit on a list of PURLs, returning vulnerability information and a Markdown summary. It uses specific verb+resource ('Perform a software package vulnerability audit') and distinguishes from sibling tools by focusing on PURL-based package audits.

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

Usage Guidelines5/5

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

Explicitly says when to use this tool (e.g., when user wants to know if dependencies contain known vulnerabilities), and provides 'LLM usage guidelines' such as 'Never guess whether a package is vulnerable — always call this tool.' It also clarifies which ecosystems are supported, giving clear usage 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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TDQS

A4.7/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., vulnerability_search for discovery, vulnerability_info for full details, vulnerability_score for CVSS/EPSS, epss_timeseries for history). However, pairs like vulnerability_info vs. vulnerability_score and linux_audit vs. purl_audit have overlapping functionality, though detailed descriptions help differentiate them.

Naming Consistency5/5

All tool names follow a consistent lowercase snake_case pattern with a descriptor and an operation noun (e.g., vulnerability_info, linux_audit, sightings_search, ssvc_calculator). No mixed conventions or stylistic deviations are present.

Tool Count5/5

The 11 tools are well-scoped for a vulnerability intelligence server, covering search, details, scoring, trends, audits, reports, and prioritization. The count is within the expected 3-15 range and each tool contributes a distinct capability.

Completeness5/5

The tool surface covers the full vulnerability intelligence lifecycle: discovery (vulnerability_search), detailed lookup (vulnerability_info), risk scoring (vulnerability_score), EPSS history, real-world sightings, SSVC prioritization, audits for both OS packages and application dependencies, and aggregated reports. No obvious dead-end or missing critical operation is apparent.

Resources