ZEN SecDB

linux_audit

Perform a Linux package vulnerability audit using SecDB.

What this tool does

Analyzes the installed packages of a Linux system-identified by OS and OS version-and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided by the user.

When to use this tool

Use this tool when the user wants to determine:

whether installed packages contain known vulnerabilities
whether a host, VM, container, or base image is affected by security advisories
which packages require patching or upgrading

If the user does not know the valid values for os or version, first call the linux_os tool to retrieve the exact supported combinations.

Inputs

os: Linux distribution identifier supported by SecDB (use linux_os to obtain allowed values).
version: OS version or codename corresponding to the selected distribution.
packages: list of installed packages, one per line, generated using the appropriate system command:

For RPM-based distributions (RHEL, CentOS, Rocky, Alma, SUSE)

rpm -qa --qf '%{NAME}-%{VERSION}-%{RELEASE}.%{ARCH}\n'

For DEB-based distributions (Ubuntu, Debian)

dpkg-query -W -f='${Package} ${Version} ${Architecture}\n'

For Alpine Linux

apk list -I

The raw output of these commands can be passed directly as the packages input (one package per line).

...
python3 3.12.3-0ubuntu2.1 amd64
systemd 255.4-1ubuntu8.10 amd64
tmux 3.4-1ubuntu0.1 amd64
...

Outputs

report: structured 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 for Linux audits.
If os or version is unclear or missing, call linux_os and ask the user to choose a valid combination.
Normalize the package list to “one entry per line” if the user provides unstructured output.
The summary is already Markdown and can be shown directly.
Use report when deeper technical analysis is required.

Input Schema

TableJSON Schema

Name	Required	Description
`os`	Yes	Linux distribution identifier supported by SecDB (use `linux_os` to obtain allowed values)
`version`	Yes	OS version or codename corresponding to the selected distribution
`packages`	Yes	list of installed packages, one per line, generated using the appropriate system command

Output Schema

TableJSON Schema

Name	Required	Description	Default
`report`	No
`summary`	No	Markdown 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?

With no annotations provided, the description carries the full behavioral disclosure burden. It clearly states that 'audit results are based exclusively on the package list provided by the user,' setting expectations about scope and limitations. It also describes the two outputs (report and summary) and notes the summary is ready-to-show Markdown, which is valuable contextual information.

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 structured with clear headings, bullet points, and code blocks. It is longer than average, but every section earns its place: the one-line summary front-loads the purpose, and the subsequent sections provide necessary commands, usage context, and output details. No redundant or filler sentences exist.

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?

The description covers input preparation (exact commands per distro), validation (call linux_os for valid values), normalization (one entry per line), output interpretation (report vs. summary), and behavioral caveats (exclusivity on provided packages). Combined with the output schema existence and high schema coverage, this tool is fully specified for an agent to invoke correctly.

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 the schema already covers all three parameters, the description significantly enriches them. For 'packages' it provides per-distro commands and an example format; for 'os' and 'version' it points to linux_os to obtain authoritative values. This goes well beyond the schema descriptions, making the parameters actionable.

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 opens with a specific verb and resource: 'Perform a Linux package vulnerability audit using SecDB.' It clearly distinguishes this tool from sibling linux_os by explicitly instructing users to call linux_os for valid OS values. The scope (analyzing installed packages and returning vulnerability info plus a Markdown summary) is precise and unambiguous.

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?

A dedicated 'When to use this tool' section lists three concrete use cases (known vulnerabilities, affected hosts/VMs/containers/base images, patching needs). It explicitly directs users to first call linux_os when os/version values are unknown, and provides exact package-list generation commands for RPM-, DEB-, and Alpine-based systems. The LLM guidelines further clarify when to always use this tool.

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

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